Publications

2025

E. Dasdemir, I. Veletic, C. P. Ly, A. E. Quesada, C. D. Pacheco, F. Z. Jelloul, P. Borges, S. Basu, S. Jindal, Z. Wang, A. Lazar, K. M. Wani, D. A. Antunes, P. K. Reville, P. H. Gunaratne, R. J. Tower, P. Sharma, and H. A. Abbas, “Integrative spatial multi-omics reveal niche-specific inflammatory signaling and differentiation hierarchies in AML,” iScience, vol. 29, no. 1, p. 114289, Nov. 2025.

pdf publisher

BibTeX

@article{dasdemir2025-spatial-aml,
  author = "Dasdemir, E. and Veletic, I. and Ly, C. P. and Quesada, A. E. and Pacheco, C. D. and Jelloul, F. Z. and Borges, P. and Basu, S. and Jindal, S. and Wang, Z. and Lazar, A. and Wani, K. M. and Antunes, D. A. and Reville, P. K. and Gunaratne, P. H. and Tower, R. J. and Sharma, P. and Abbas, H. A.",
  title = "Integrative spatial multi-omics reveal niche-specific inflammatory signaling and differentiation hierarchies in AML",
  year = "2025",
  doi = "10.1016/j.isci.2025.114289",
  pmid = "41536977",
  month = nov,
  volume = "29",
  number = "1",
  pages = "114289",
  journal = "iScience",
  abstract = "Acute myeloid leukemia (AML) is a clonal disorder characterized by immature blasts and arrested differentiation that primarily affects the bone marrow (BM) and occasionally presents as extramedullary (EM) disease. EM manifestations highlight AML's adaptability to distinct microenvironments, which we examined using spatial analyses of medullary and EM tissues. We describe a workflow for Visium-based spatial transcriptomics in medullary and EM AML, revealing insights into cell-cell communication and the spatial organization of AML hierarchies. In BM, monocytes and granulocyte-monocyte progenitors colocalized with leukemic populations, sharing molecular signatures with those in EM sample. CXCL12-CXCR4-mediated communication correlated with PI3K/AKT/mTOR signaling in inflammatory niches. Trans-differentiation signals concentrated in AML-infiltrated regions; committed-like AML cells resided in inflammatory niches distant from trabeculae, while primitive-like cells localized near the endosteal niche. GeoMX digital spatial profiling and Opal multiplex fluorescent immunohistochemistry provided orthogonal validation. Overall, our study offers a valuable multimodal resource for exploring AML spatial biology with potential applications in other BM malignancies."
}

Abstract

Acute myeloid leukemia (AML) is a clonal disorder characterized by immature blasts and arrested differentiation that primarily affects the bone marrow (BM) and occasionally presents as extramedullary (EM) disease. EM manifestations highlight AML’s adaptability to distinct microenvironments, which we examined using spatial analyses of medullary and EM tissues. We describe a workflow for Visium-based spatial transcriptomics in medullary and EM AML, revealing insights into cell-cell communication and the spatial organization of AML hierarchies. In BM, monocytes and granulocyte-monocyte progenitors colocalized with leukemic populations, sharing molecular signatures with those in EM sample. CXCL12-CXCR4-mediated communication correlated with PI3K/AKT/mTOR signaling in inflammatory niches. Trans-differentiation signals concentrated in AML-infiltrated regions; committed-like AML cells resided in inflammatory niches distant from trabeculae, while primitive-like cells localized near the endosteal niche. GeoMX digital spatial profiling and Opal multiplex fluorescent immunohistochemistry provided orthogonal validation. Overall, our study offers a valuable multimodal resource for exploring AML spatial biology with potential applications in other BM malignancies.

F. J. F. de Sousa, D. A. Antunes, and G. Zanatta, “PI3K-Seeker: A Machine Learning-Powered Web Tool to Discover PI3K Inhibitors,” ACS Omega, vol. 10, no. 47, pp. 57255–57266, Nov. 2025.

pdf publisher

BibTeX

@article{desousa2025-pi3k,
  author = "de Sousa, F. J. F. and Antunes, D. A. and Zanatta, G.",
  title = "PI3K-Seeker: A Machine Learning-Powered Web Tool to Discover PI3K Inhibitors",
  year = "2025",
  doi = "10.1021/acsomega.5c07315",
  pmid = "41358069",
  month = nov,
  volume = "10",
  number = "47",
  pages = "57255-57266",
  publisher = "American Chemical Society",
  journal = "ACS Omega",
  abstract = "Phosphatidylinositol 3-kinases (PI3Ks) play a crucial role in human metabolism, and their dysregulation contributes to the development of several metabolic disorders, including cancer. Despite advances in experimental high-throughput screening, discovering new therapeutic agents remains challenging and costly. In this study, we developed PI3K-Seeker, a web server based on a two-stage prediction process to find new PI3K inhibitors. The first stage eliminates nonbinders, while the second refines the selection, leaving only molecules with a high probability of being potent inhibitors. Models were trained using the XGBoost algorithm and PubChem fingerprints extracted from distinct datasets. In the first stage of classification, the model showed impressive metrics (MCC: 0.917, AUC-ROC: 0.993, and ACC: 0.917). In the second stage, the data enhancement, the model trained also performed exceptionally well (MCC: 0.939, AUC-ROC: 0.956, and ACC: 0.994). The PI3K-Seeker is a user-friendly web server suitable for a large set of compounds, available at http://www.ufrgs.br/labec/pi3k-seeker/."
}

Abstract

Phosphatidylinositol 3-kinases (PI3Ks) play a crucial role in human metabolism, and their dysregulation contributes to the development of several metabolic disorders, including cancer. Despite advances in experimental high-throughput screening, discovering new therapeutic agents remains challenging and costly. In this study, we developed PI3K-Seeker, a web server based on a two-stage prediction process to find new PI3K inhibitors. The first stage eliminates nonbinders, while the second refines the selection, leaving only molecules with a high probability of being potent inhibitors. Models were trained using the XGBoost algorithm and PubChem fingerprints extracted from distinct datasets. In the first stage of classification, the model showed impressive metrics (MCC: 0.917, AUC-ROC: 0.993, and ACC: 0.917). In the second stage, the data enhancement, the model trained also performed exceptionally well (MCC: 0.939, AUC-ROC: 0.956, and ACC: 0.994). The PI3K-Seeker is a user-friendly web server suitable for a large set of compounds, available at http://www.ufrgs.br/labec/pi3k-seeker/.

M. B. Castillo, S. Rankothgedera, S. Thevasagayampillai, A. Kandasamy, J. Lewis, C. Woody, M. Vaz de Freitas, D. A. Antunes, R. El-Zein, and P. H. Gunaratne, “Identification of immunogenic KIF5B-RET fusion neopeptides driving immune stimulation in tumor specific CD8+ T cells,” Frontiers in Immunology, vol. 16, p. 1635810, Oct. 2025.

pdf publisher

BibTeX

@article{castillo2025-kif5b,
  author = "Castillo, M. B. and Rankothgedera, S. and Thevasagayampillai, S. and Kandasamy, A. and Lewis, J. and Woody, C. and Vaz de Freitas, M. and Antunes, D. A. and El-Zein, R. and Gunaratne, P. H.",
  title = "Identification of immunogenic KIF5B-RET fusion neopeptides driving immune stimulation in tumor specific CD8+ T cells",
  year = "2025",
  doi = "10.3389/fimmu.2025.1635810",
  pmid = "41246330",
  month = oct,
  volume = "16",
  pages = "1635810",
  journal = "Frontiers in Immunology",
  abstract = "INTRODUCTION: Non-classical neoantigens at the fusion junctions of chimeric RNAs are tumor- specific with a low risk of autoimmunity and therefore represent ideal targets for personalized vaccines. We present a platform to discover immunogenic neoantigens that drive CD8+ T cell clonotypes from chimeric RNA fusion junctions to promote tumor-reactive T cell expansion and prevent tumor recurrence following immunotherapies. METHODS: RNA sequencing data from 15 Lung Adenocarcinoma and 15 Squamous Cell Carcinoma patients (tumor and adjacent normal tissues) were analyzed. The KIF5B [Exon 1-15] | RET [Exon 12- 19] fusion was selected from a patient-derived xenograft (PDX) model based on its established role as an actionable cancer driver in an independent tumor with the same junction. We assessed the affinity of neopeptides from the KIF5B-RET fusion to MHC Class I molecules using in silico tools MHCNuggets and MixMHCPred 2. RESULTS: HLA-C07:02 showed the highest affinity for 9-mer peptides with NNDVKEDPK, which emerged as the strongest binder based on HLA-Arena docking and binding energy calculations. Immunogenicity was evaluated by IFNg Enzyme-Linked Immunosorbent Spot (ELISpot) assays using HLA-C07:02- matched Peripheral Blood Mononuclear Cells (PBMCs) from two donors. CD8+ T cells from both donors responded to specific junction peptides. Single-cell 5'gene expression RNA sequencing and T Cell receptor mapping of activated T cells identified 15 TCR clonotypes, five of which had high activation. Key residues in CDR3a and CDR3b are crucial for CD8+ T cell activation. NNDVKEDPK and KEDPKWEFP showed minimal cross-reactivity with the normal tissues. DISCUSSION: This study demonstrates a robust pipeline for identifying and validating immunogenic neoantigens from chimeric RNAs to design personalized cancer vaccines with high immunogenicity and low cross-reactivity."
}

Abstract

INTRODUCTION: Non-classical neoantigens at the fusion junctions of chimeric RNAs are tumor- specific with a low risk of autoimmunity and therefore represent ideal targets for personalized vaccines. We present a platform to discover immunogenic neoantigens that drive CD8+ T cell clonotypes from chimeric RNA fusion junctions to promote tumor-reactive T cell expansion and prevent tumor recurrence following immunotherapies. METHODS: RNA sequencing data from 15 Lung Adenocarcinoma and 15 Squamous Cell Carcinoma patients (tumor and adjacent normal tissues) were analyzed. The KIF5B [Exon 1-15] | RET [Exon 12- 19] fusion was selected from a patient-derived xenograft (PDX) model based on its established role as an actionable cancer driver in an independent tumor with the same junction. We assessed the affinity of neopeptides from the KIF5B-RET fusion to MHC Class I molecules using in silico tools MHCNuggets and MixMHCPred 2. RESULTS: HLA-C07:02 showed the highest affinity for 9-mer peptides with NNDVKEDPK, which emerged as the strongest binder based on HLA-Arena docking and binding energy calculations. Immunogenicity was evaluated by IFNg Enzyme-Linked Immunosorbent Spot (ELISpot) assays using HLA-C07:02- matched Peripheral Blood Mononuclear Cells (PBMCs) from two donors. CD8+ T cells from both donors responded to specific junction peptides. Single-cell 5’gene expression RNA sequencing and T Cell receptor mapping of activated T cells identified 15 TCR clonotypes, five of which had high activation. Key residues in CDR3a and CDR3b are crucial for CD8+ T cell activation. NNDVKEDPK and KEDPKWEFP showed minimal cross-reactivity with the normal tissues. DISCUSSION: This study demonstrates a robust pipeline for identifying and validating immunogenic neoantigens from chimeric RNAs to design personalized cancer vaccines with high immunogenicity and low cross-reactivity.

P. Arman, Z. Haghighijoo, C. A. Lupascu, A. K. Singh, N. A. Goode, T. J. Baumgartner, J. Singh, Y. Xue, P. Wang, H. Chen, D. A. Antunes, M. Lijffijt, J. Zhou, M. Migliore, and F. Laezza, “FGF14 Peptide Derivative Differentially Regulates Nav1.2 and Na(v)1.6 Function,” Life (Basel), vol. 15, no. 9, p. 1345, Aug. 2025.

pdf publisher

BibTeX

@article{arman2025-fgf14,
  author = "Arman, P. and Haghighijoo, Z. and Lupascu, C. A. and Singh, A. K. and Goode, N. A. and Baumgartner, T. J. and Singh, J. and Xue, Y. and Wang, P. and Chen, H. and Antunes, D. A. and Lijffijt, M. and Zhou, J. and Migliore, M. and Laezza, F.",
  title = "FGF14 Peptide Derivative Differentially Regulates Nav1.2 and Na(v)1.6 Function",
  year = "2025",
  doi = "10.3390/life15091345",
  pmid = "41010287",
  month = aug,
  volume = "15",
  number = "9",
  pages = "1345",
  journal = "Life (Basel)",
  abstract = "Voltage-gated Na+ channels (Nav) are the molecular determinants of action potential initiation and propagation. Among the nine voltage-gated Na+ channel isoforms (Nav1.1-Nav1.9), Nav1.2 and Nav1.6 are of particular interest because of their developmental expression profile throughout the central nervous system (CNS) and their association with channelopathies. Although the α-subunit coded by each of the nine isoforms can sufficiently confer transient Na+ currents (INa), in vivo these channels are modulated by auxiliary proteins like intracellular fibroblast growth factor (iFGFs) through protein-protein interaction (PPI), and probes developed from iFGF/Nav PPI complexes have been shown to precisely modulate Nav channels. Previous studies identified ZL0177, a peptidomimetic derived from a short peptide sequence at the FGF14/Nav1.6 PPI interface, as a functional modulator of Nav1.6-mediated INa+. However, the isoform specificity, binding sites, and putative physiological impact of ZL0177 on neuronal excitability remain unexplored. Here, we used automated planar patch-clamp electrophysiology to assess ZL0177's functional activity in cells stably expressing Nav1.2 or Nav1.6. While ZL0177 was found to suppress INa in both Nav1.2- and Nav1.6-expressing cells, ZL0177 elicited functionally divergent effects on channel kinetics that were isoform-specific and supported by differential docking of the compound to AlphaFold structures of the two channel isoforms. Computational modeling predicts that ZL0177 modulates Nav1.2 and Nav1.6 in an isoform-specific manner, eliciting phenotypically divergent effects on action potential discharge. Taken together, these results highlight the potential of PPI derivatives for isoform-specific regulation of Nav channels and the development of therapeutics for channelopathies."
}

Abstract

Voltage-gated Na+ channels (Nav) are the molecular determinants of action potential initiation and propagation. Among the nine voltage-gated Na+ channel isoforms (Nav1.1-Nav1.9), Nav1.2 and Nav1.6 are of particular interest because of their developmental expression profile throughout the central nervous system (CNS) and their association with channelopathies. Although the α-subunit coded by each of the nine isoforms can sufficiently confer transient Na+ currents (INa), in vivo these channels are modulated by auxiliary proteins like intracellular fibroblast growth factor (iFGFs) through protein-protein interaction (PPI), and probes developed from iFGF/Nav PPI complexes have been shown to precisely modulate Nav channels. Previous studies identified ZL0177, a peptidomimetic derived from a short peptide sequence at the FGF14/Nav1.6 PPI interface, as a functional modulator of Nav1.6-mediated INa+. However, the isoform specificity, binding sites, and putative physiological impact of ZL0177 on neuronal excitability remain unexplored. Here, we used automated planar patch-clamp electrophysiology to assess ZL0177’s functional activity in cells stably expressing Nav1.2 or Nav1.6. While ZL0177 was found to suppress INa in both Nav1.2- and Nav1.6-expressing cells, ZL0177 elicited functionally divergent effects on channel kinetics that were isoform-specific and supported by differential docking of the compound to AlphaFold structures of the two channel isoforms. Computational modeling predicts that ZL0177 modulates Nav1.2 and Nav1.6 in an isoform-specific manner, eliciting phenotypically divergent effects on action potential discharge. Taken together, these results highlight the potential of PPI derivatives for isoform-specific regulation of Nav channels and the development of therapeutics for channelopathies.

2024

C. C. Alves, J. Lewis, D. A. Antunes, and E. A. Donadi, “The Role of Vimentin Peptide Citrullination in the Structure and Dynamics of HLA-DRB1 Rheumatoid Arthritis Risk-Associated Alleles,” International Journal of Molecular Sciences, vol. 26, no. 1, p. 34, Dec. 2024.

pdf publisher

BibTeX

@article{alves2024-vimentin,
  author = "Alves, C. C. and Lewis, J. and Antunes, D. A. and Donadi, E. A.",
  title = "The Role of Vimentin Peptide Citrullination in the Structure and Dynamics of HLA-DRB1 Rheumatoid Arthritis Risk-Associated Alleles",
  year = "2024",
  doi = "10.3390/ijms26010034",
  pmid = "39795892",
  month = dec,
  volume = "26",
  number = "1",
  pages = "34",
  journal = "International Journal of Molecular Sciences",
  abstract = "Citrullination, a post-translational modification (PTM), plays a critical role in rheumatoid arthritis (RA) by triggering immune responses to citrullinated self-antigens. Some HLA-DRB1 genes encode molecules with the shared epitope (QKRAA/QRRAA) sequence in the peptide-binding groove which preferentially presents citrulline-modified peptides, like vimentin, that intensifies the immune response in RA. In this study, we used computational approaches to evaluate intermolecular interactions between vimentin peptide-ligands (with/without PTM) and HLA-DRB1 alleles associated with a significantly increased risk for RA development. Crystal structures for HLA-DRB1*04:01, *04:04, and *04:05 bound to citrullinated peptides (PDB ID: 4MCY, 4MD5, 6BIR) were retrieved from the Protein Data Bank and non-citrullinated 3D structures were generated by mutating citrulline to arginine. The pHLA complexes were submitted to four rounds (50 ns each) of molecular dynamic simulations (MD) with Gromacs v.2022. Our results show that citrulline strengthens the interaction between vimentin and the HLA-DRB1 molecules, therefore impacting both the peptide affinity to the HLAs and pHLA stability; it also induces more intermolecular hydrogen bond formation during MD in the pHLA. Citrulline prevents repulsion between amino acid 71β and the P4-residue of native vimentin. Thus, vimentin citrullination seems to affect pHLA binding and dynamics, which may influence RA-related immune responses."
}

Abstract

Citrullination, a post-translational modification (PTM), plays a critical role in rheumatoid arthritis (RA) by triggering immune responses to citrullinated self-antigens. Some HLA-DRB1 genes encode molecules with the shared epitope (QKRAA/QRRAA) sequence in the peptide-binding groove which preferentially presents citrulline-modified peptides, like vimentin, that intensifies the immune response in RA. In this study, we used computational approaches to evaluate intermolecular interactions between vimentin peptide-ligands (with/without PTM) and HLA-DRB1 alleles associated with a significantly increased risk for RA development. Crystal structures for HLA-DRB1*04:01, *04:04, and *04:05 bound to citrullinated peptides (PDB ID: 4MCY, 4MD5, 6BIR) were retrieved from the Protein Data Bank and non-citrullinated 3D structures were generated by mutating citrulline to arginine. The pHLA complexes were submitted to four rounds (50 ns each) of molecular dynamic simulations (MD) with Gromacs v.2022. Our results show that citrulline strengthens the interaction between vimentin and the HLA-DRB1 molecules, therefore impacting both the peptide affinity to the HLAs and pHLA stability; it also induces more intermolecular hydrogen bond formation during MD in the pHLA. Citrulline prevents repulsion between amino acid 71β and the P4-residue of native vimentin. Thus, vimentin citrullination seems to affect pHLA binding and dynamics, which may influence RA-related immune responses.

H. N. Le, M. V. de Freitas, and D. A. Antunes, “Strengths and limitations of web servers for the modeling of TCRpMHC complexes,” Computational and Structural Biotechnology Journal, vol. 23, pp. 2938–2948, July 2024.

pdf publisher

BibTeX

@article{le2024-tcrpmhc,
  author = "Le, H. N. and de Freitas, M. V. and Antunes, D. A.",
  title = "Strengths and limitations of web servers for the modeling of TCRpMHC complexes",
  year = "2024",
  doi = "10.1016/j.csbj.2024.06.028",
  pmid = "39104710",
  month = jul,
  volume = "23",
  pages = "2938-2948",
  publisher = "Elsevier B.V.",
  journal = "Computational and Structural Biotechnology Journal",
  abstract = "Cellular immunity relies on the ability of a T-cell receptor (TCR) to recognize a peptide (p) presented by a class I major histocompatibility complex (MHC) receptor on the surface of a cell. The TCR-peptide-MHC (TCRpMHC) interaction is a crucial step in activating T-cells, and the structural characteristics of these molecules play a significant role in determining the specificity and affinity of this interaction. Hence, obtaining 3D structures of TCRpMHC complexes offers valuable insights into various aspects of cellular immunity and can facilitate the development of T-cell-based immunotherapies. Here, we aimed to compare three popular web servers for modeling the structures of TCRpMHC complexes, namely ImmuneScape (IS), TCRpMHCmodels, and TCRmodel2, to examine their strengths and limitations. Each method employs a different modeling strategy, including docking, homology modeling, and deep learning. The accuracy of each method was evaluated by reproducing the 3D structures of a dataset of 87 TCRpMHC complexes with experimentally determined crystal structures available on the Protein Data Bank (PDB). All selected structures were limited to human MHC alleles, presenting a diverse set of peptide ligands. A detailed analysis of produced models was conducted using multiple metrics, including Root Mean Square Deviation (RMSD) and standardized assessments from CAPRI and DockQ. Special attention was given to the complementarity-determining region (CDR) loops of the TCRs and to the peptide ligands, which define most of the unique features and specificity of a given TCRpMHC interaction. Our study provides an optimistic view of the current state-of-the-art for TCRpMHC modeling but highlights some remaining challenges that must be addressed in order to support the future application of these tools for TCR engineering and computer-aided design of TCR-based immunotherapies."
}

Abstract

Cellular immunity relies on the ability of a T-cell receptor (TCR) to recognize a peptide (p) presented by a class I major histocompatibility complex (MHC) receptor on the surface of a cell. The TCR-peptide-MHC (TCRpMHC) interaction is a crucial step in activating T-cells, and the structural characteristics of these molecules play a significant role in determining the specificity and affinity of this interaction. Hence, obtaining 3D structures of TCRpMHC complexes offers valuable insights into various aspects of cellular immunity and can facilitate the development of T-cell-based immunotherapies. Here, we aimed to compare three popular web servers for modeling the structures of TCRpMHC complexes, namely ImmuneScape (IS), TCRpMHCmodels, and TCRmodel2, to examine their strengths and limitations. Each method employs a different modeling strategy, including docking, homology modeling, and deep learning. The accuracy of each method was evaluated by reproducing the 3D structures of a dataset of 87 TCRpMHC complexes with experimentally determined crystal structures available on the Protein Data Bank (PDB). All selected structures were limited to human MHC alleles, presenting a diverse set of peptide ligands. A detailed analysis of produced models was conducted using multiple metrics, including Root Mean Square Deviation (RMSD) and standardized assessments from CAPRI and DockQ. Special attention was given to the complementarity-determining region (CDR) loops of the TCRs and to the peptide ligands, which define most of the unique features and specificity of a given TCRpMHC interaction. Our study provides an optimistic view of the current state-of-the-art for TCRpMHC modeling but highlights some remaining challenges that must be addressed in order to support the future application of these tools for TCR engineering and computer-aided design of TCR-based immunotherapies.

R. Fasoulis, M. M. Rigo, G. Lizée, D. A. Antunes, and L. E. Kavraki, “APE-Gen2.0: Expanding Rapid Class I Peptide-Major Histocompatibility Complex Modeling to Post-Translational Modifications and Noncanonical Peptide Geometries,” Journal of Chemical Information and Modeling, vol. 64, no. 5, pp. 1730–1750, Mar. 2024.

pdf publisher

BibTeX

@article{fasoulis2024-apengen2,
  author = "Fasoulis, R. and Rigo, M. M. and Lizée, G. and Antunes, D. A. and Kavraki, L. E.",
  title = "APE-Gen2.0: Expanding Rapid Class I Peptide-Major Histocompatibility Complex Modeling to Post-Translational Modifications and Noncanonical Peptide Geometries",
  year = "2024",
  doi = "10.1021/acs.jcim.3c01667",
  pmid = "38415656",
  month = mar,
  volume = "64",
  number = "5",
  pages = "1730-1750",
  journal = "Journal of Chemical Information and Modeling",
  abstract = "The recognition of peptides bound to class I major histocompatibility complex (MHC-I) receptors by T-cell receptors (TCRs) is a determinant of triggering the adaptive immune response. While the exact molecular features that drive the TCR recognition are still unknown, studies have suggested that the geometry of the joint peptide-MHC (pMHC) structure plays an important role. As such, there is a definite need for methods and tools that accurately predict the structure of the peptide bound to the MHC-I receptor. In the past few years, many pMHC structural modeling tools have emerged that provide high-quality modeled structures in the general case. However, there are numerous instances of non-canonical cases in the immunopeptidome that the majority of pMHC modeling tools do not attend to, most notably, peptides that exhibit non-standard amino acids and post-translational modifications (PTMs) or peptides that assume non-canonical geometries in the MHC binding cleft. Such chemical and structural properties have been shown to be present in neoantigens; therefore, accurate structural modeling of these instances can be vital for cancer immunotherapy. To this end, we have developed APE-Gen2.0, a tool that improves upon its predecessor and other pMHC modeling tools, both in terms of modeling accuracy and the available modeling range of non-canonical peptide cases. Some of the improvements include (i) the ability to model peptides that have different types of PTMs such as phosphorylation, nitration, and citrullination; (ii) a new and improved anchor identification routine in order to identify and model peptides that exhibit a non-canonical anchor conformation; and (iii) a web server that provides a platform for easy and accessible pMHC modeling. We further show that structures predicted by APE-Gen2.0 can be used to assess the effects that PTMs have in binding affinity in a more accurate manner than just using solely the sequence of the peptide. APE-Gen2.0 is freely available at https://apegen.kavrakilab.org."
}

Abstract

The recognition of peptides bound to class I major histocompatibility complex (MHC-I) receptors by T-cell receptors (TCRs) is a determinant of triggering the adaptive immune response. While the exact molecular features that drive the TCR recognition are still unknown, studies have suggested that the geometry of the joint peptide-MHC (pMHC) structure plays an important role. As such, there is a definite need for methods and tools that accurately predict the structure of the peptide bound to the MHC-I receptor. In the past few years, many pMHC structural modeling tools have emerged that provide high-quality modeled structures in the general case. However, there are numerous instances of non-canonical cases in the immunopeptidome that the majority of pMHC modeling tools do not attend to, most notably, peptides that exhibit non-standard amino acids and post-translational modifications (PTMs) or peptides that assume non-canonical geometries in the MHC binding cleft. Such chemical and structural properties have been shown to be present in neoantigens; therefore, accurate structural modeling of these instances can be vital for cancer immunotherapy. To this end, we have developed APE-Gen2.0, a tool that improves upon its predecessor and other pMHC modeling tools, both in terms of modeling accuracy and the available modeling range of non-canonical peptide cases. Some of the improvements include (i) the ability to model peptides that have different types of PTMs such as phosphorylation, nitration, and citrullination; (ii) a new and improved anchor identification routine in order to identify and model peptides that exhibit a non-canonical anchor conformation; and (iii) a web server that provides a platform for easy and accessible pMHC modeling. We further show that structures predicted by APE-Gen2.0 can be used to assess the effects that PTMs have in binding affinity in a more accurate manner than just using solely the sequence of the peptide. APE-Gen2.0 is freely available at https://apegen.kavrakilab.org.

R. Fasoulis, M. M. Rigo, D. A. Antunes, G. Paliouras, and L. E. Kavraki, “Transfer learning improves pMHC kinetic stability and immunogenicity predictions,” Immunoinformatics (Amst), vol. 13, p. 100030, Mar. 2024.

pdf publisher

BibTeX

@article{fasoulis2024-tlmhc,
  author = "Fasoulis, R. and Rigo, M. M. and Antunes, D. A. and Paliouras, G. and Kavraki, L. E.",
  title = "Transfer learning improves pMHC kinetic stability and immunogenicity predictions",
  year = "2024",
  doi = "https://www.sciencedirect.com/science/article/pii/S2667119023000101",
  pmid = "38577265",
  month = mar,
  volume = "13",
  pages = "100030",
  journal = "Immunoinformatics (Amst)",
  abstract = "The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at https://github.com/KavrakiLab/TL-MHC."
}

Abstract

The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at https://github.com/KavrakiLab/TL-MHC.

B. Wang, P. K. Reville, M. Y. Yassouf, F. Z. Jelloul, C. Ly, P. N. Desai, Z. Wang, P. Borges, I. Veletic, E. Dasdemir, J. K. Burks, G. Tang, S. Guo, A. I. Garza, C. Nasnas, N. R. Vaughn, N. Baran, Q. Deng, J. Matthews, P. H. Gunaratne, D. A. Antunes, S. Ekmekcioglu, K. Sasaki, M. B. Garcia, B. Cuglievan, D. Hao, N. Daver, M. R. Green, M. Konopleva, A. Futreal, S. M. Post, and H. A. Abbas, “Comprehensive characterization of IFNγ signaling in acute myeloid leukemia reveals prognostic and therapeutic strategies,” Nature Communications, vol. 15, no. 1, p. 1821, Feb. 2024.

pdf publisher

BibTeX

@article{wang2024-ifng-aml,
  author = "Wang, B. and Reville, P. K. and Yassouf, M. Y. and Jelloul, F. Z. and Ly, C. and Desai, P. N. and Wang, Z. and Borges, P. and Veletic, I. and Dasdemir, E. and Burks, J. K. and Tang, G. and Guo, S. and Garza, A. I. and Nasnas, C. and Vaughn, N. R. and Baran, N. and Deng, Q. and Matthews, J. and Gunaratne, P. H. and Antunes, D. A. and Ekmekcioglu, S. and Sasaki, K. and Garcia, M. B. and Cuglievan, B. and Hao, D. and Daver, N. and Green, M. R. and Konopleva, M. and Futreal, A. and Post, S. M. and Abbas, H. A.",
  title = "Comprehensive characterization of IFNγ signaling in acute myeloid leukemia reveals prognostic and therapeutic strategies",
  year = "2024",
  doi = "10.1038/s41467-024-45916-6",
  pmid = "38418901",
  month = feb,
  volume = "15",
  number = "1",
  pages = "1821",
  journal = "Nature Communications",
  abstract = "Interferon gamma (IFNγ) is a critical cytokine known for its diverse roles in immune regulation, inflammation, and tumor surveillance. However, while IFNγ levels were elevated in sera of most newly diagnosed acute myeloid leukemia (AML) patients, its complex interplay in AML remains insufficiently understood. We aim to characterize these complex interactions through comprehensive bulk and single-cell approaches in bone marrow of newly diagnosed AML patients. We identify monocytic AML as having a unique microenvironment characterized by IFNγ producing T and NK cells, high IFNγ signaling, and immunosuppressive features. IFNγ signaling score strongly correlates with venetoclax resistance in primary AML patient cells. Additionally, IFNγ treatment of primary AML patient cells increased venetoclax resistance. Lastly, a parsimonious 47-gene IFNγ score demonstrates robust prognostic value. In summary, our findings suggest that inhibiting IFNγ is a potential treatment strategy to overcoming venetoclax resistance and immune evasion in AML patients."
}

Abstract

Interferon gamma (IFNγ) is a critical cytokine known for its diverse roles in immune regulation, inflammation, and tumor surveillance. However, while IFNγ levels were elevated in sera of most newly diagnosed acute myeloid leukemia (AML) patients, its complex interplay in AML remains insufficiently understood. We aim to characterize these complex interactions through comprehensive bulk and single-cell approaches in bone marrow of newly diagnosed AML patients. We identify monocytic AML as having a unique microenvironment characterized by IFNγ producing T and NK cells, high IFNγ signaling, and immunosuppressive features. IFNγ signaling score strongly correlates with venetoclax resistance in primary AML patient cells. Additionally, IFNγ treatment of primary AML patient cells increased venetoclax resistance. Lastly, a parsimonious 47-gene IFNγ score demonstrates robust prognostic value. In summary, our findings suggest that inhibiting IFNγ is a potential treatment strategy to overcoming venetoclax resistance and immune evasion in AML patients.

D. A. Antunes, B. M. Baker, M. Cornberg, and L. K. Selin, “Editorial: Quantification and prediction of T-cell cross-reactivity through experimental and computational methods,” Frontiers in Immunology, vol. 15, p. 1377259, Feb. 2024.

pdf publisher

BibTeX

@article{antunes2024-ed-XR,
  author = "Antunes, D. A. and Baker, B. M. and Cornberg, M. and Selin, L. K.",
  title = "Editorial: Quantification and prediction of T-cell cross-reactivity through experimental and computational methods",
  year = "2024",
  doi = "10.3389/fimmu.2024.1377259",
  pmid = "38444853",
  month = feb,
  volume = "15",
  pages = "1377259",
  journal = "Frontiers in Immunology",
  abstract = "Editorial on the Research Topic Quantification and prediction of T-cell cross-reactivity through experimental and computational methods."
}

Abstract

Editorial on the Research Topic Quantification and prediction of T-cell cross-reactivity through experimental and computational methods.

2023

G. Bebis, M. Kato, M. Kohandel, K. Wilkie, D. A. Antunes, K. Chen, and J. Dou, “Editorial: Advances in mathematical and computational oncology, volume III,” Frontiers in Oncology, vol. 13, p. 1282882, Sept. 2023.

pdf publisher

BibTeX

@article{bebis2023-ed-math-onco,
  author = "Bebis, G. and Kato, M. and Kohandel, M. and Wilkie, K. and Antunes, D. A. and Chen, K. and Dou, J.",
  title = "Editorial: Advances in mathematical and computational oncology, volume III",
  year = "2023",
  doi = "10.3389/fonc.2023.1282882",
  pmid = "37817766",
  month = sep,
  volume = "13",
  pages = "1282882",
  journal = "Frontiers in Oncology",
  abstract = "Editorial on the Research Topic Advances in mathematical and computational oncology, volume III."
}

Abstract

Editorial on the Research Topic Advances in mathematical and computational oncology, volume III.

D. A. Antunes, C. T. Schoeder, M. Baek, and E. A. Donadi, “Editorial: Structural modeling and computational analyses of immune system molecules,” Frontiers in Immunology, vol. 14, p. 1274670, Sept. 2023.

pdf publisher

BibTeX

@article{antunes2023-ed-mod,
  author = "Antunes, D. A. and Schoeder, C. T. and Baek, M. and Donadi, E. A.",
  title = "Editorial: Structural modeling and computational analyses of immune system molecules",
  year = "2023",
  doi = "10.3389/fimmu.2023.1274670",
  pmid = "37731492",
  month = sep,
  volume = "14",
  pages = "1274670",
  journal = "Frontiers in Immunology",
  abstract = "Editorial on the Research Topic Structural modeling and computational analyses of immune system molecules."
}

Abstract

Editorial on the Research Topic Structural modeling and computational analyses of immune system molecules.

C. C. Alves, T. Arns, M. L. Oliveira, P. Moreau, D. A. Antunes, E. C. Castelli, C. T. Mendes-Junior, S. Giuliatti, and E. A. Donadi, “Computational and atomistic studies applied to the understanding of the structural and behavioral features of the immune checkpoint HLA-G molecule and gene,” Human Immunology, vol. 84, no. 8, pp. 374–383, Aug. 2023.

publisher

BibTeX

@article{alves2023-hlag,
  author = "Alves, C. C. and Arns, T. and Oliveira, M. L. and Moreau, P. and Antunes, D. A. and Castelli, E. C. and Mendes-Junior, C. T. and Giuliatti, S. and Donadi, E. A.",
  title = "Computational and atomistic studies applied to the understanding of the structural and behavioral features of the immune checkpoint HLA-G molecule and gene",
  year = "2023",
  doi = "10.1016/j.humimm.2023.01.004",
  pmid = "36710086",
  month = aug,
  volume = "84",
  number = "8",
  pages = "374-383",
  publisher = "Elsevier Inc.",
  journal = "Human Immunology",
  abstract = "We took advantage of the increasingly evolving approaches for in silico studies concerning protein structures, protein molecular dynamics (MD), protein-protein and protein-DNA docking to evaluate: (i) the structure and MD characteristics of the HLA-G well-recognized isoforms, (ii) the impact of missense mutations at HLA-G receptor genes (LILRB1/2), and (iii) the differential binding of the hypoxia-inducible factor 1 (HIF1) to hypoxia-responsive elements (HRE) at the HLA-G gene. Besides reviewing these topics, they were revisited including the following novel results: (i) the HLA-G6 isoforms were unstable docked or not with β2-microglobulin or peptide, (ii) missense mutations at LILRB1/2 genes, exchanging amino acids at the intracellular domain, particularly those located within and around the ITIM motifs, may impact the HLA-G binding strength, and (iii) HREs motifs at the HLA-G promoter or exon 2 regions exhibiting a guanine at their third position present a higher affinity for HIF1 when compared to an adenine at the same position. These data shed some light into the functional aspects of HLA-G, particularly how polymorphisms may influence the role of the molecule. Computational and atomistic studies have provided alternative tools for experimental physical methodologies, which are time-consuming, expensive, demanding large quantities of purified proteins, and exhibit low output."
}

Abstract

We took advantage of the increasingly evolving approaches for in silico studies concerning protein structures, protein molecular dynamics (MD), protein-protein and protein-DNA docking to evaluate: (i) the structure and MD characteristics of the HLA-G well-recognized isoforms, (ii) the impact of missense mutations at HLA-G receptor genes (LILRB1/2), and (iii) the differential binding of the hypoxia-inducible factor 1 (HIF1) to hypoxia-responsive elements (HRE) at the HLA-G gene. Besides reviewing these topics, they were revisited including the following novel results: (i) the HLA-G6 isoforms were unstable docked or not with β2-microglobulin or peptide, (ii) missense mutations at LILRB1/2 genes, exchanging amino acids at the intracellular domain, particularly those located within and around the ITIM motifs, may impact the HLA-G binding strength, and (iii) HREs motifs at the HLA-G promoter or exon 2 regions exhibiting a guanine at their third position present a higher affinity for HIF1 when compared to an adenine at the same position. These data shed some light into the functional aspects of HLA-G, particularly how polymorphisms may influence the role of the molecule. Computational and atomistic studies have provided alternative tools for experimental physical methodologies, which are time-consuming, expensive, demanding large quantities of purified proteins, and exhibit low output.

A. Conev, M. M. Rigo, D. Devaurs, A. F. Fonseca, H. Kalavadwala, M. V. de Freitas, C. Clementi, G. Zanatta, D. A. Antunes, and L. E. Kavraki, “EnGens: a computational framework for generation and analysis of representative protein conformational ensembles,” Briefings in Bioinformatics, vol. 24, no. 4, July 2023.

pdf publisher

BibTeX

@article{conev2023-engens,
  author = "Conev, A. and Rigo, M. M. and Devaurs, D. and Fonseca, A. F. and Kalavadwala, H. and de Freitas, M. V. and Clementi, C. and Zanatta, G. and Antunes, D. A. and Kavraki, L. E.",
  title = "EnGens: a computational framework for generation and analysis of representative protein conformational ensembles",
  year = "2023",
  doi = "10.1093/bib/bbad242",
  pmid = "37418278",
  month = jul,
  volume = "24",
  number = "4",
  publisher = "Oxford University Press",
  journal = "Briefings in Bioinformatics",
  abstract = "Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in the number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing representative protein conformational ensembles. In this work, we: (1) provide an overview of existing methods and tools for representative protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples from the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations."
}

Abstract

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in the number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing representative protein conformational ensembles. In this work, we: (1) provide an overview of existing methods and tools for representative protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples from the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.

A. F. Fonseca and D. A. Antunes, “CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases,” Frontiers in Immunology, vol. 14, p. 1142573, June 2023.

pdf publisher

BibTeX

@article{fonseca2023-crossdome,
  author = "Fonseca, A. F. and Antunes, D. A.",
  title = "CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases",
  year = "2023",
  doi = "10.3389/fimmu.2023.1142573",
  pmid = "37377956",
  month = jun,
  volume = "14",
  pages = "1142573",
  journal = "Frontiers in Immunology",
  abstract = "T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome."
}

Abstract

T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome.

S. E. Lee, F. Wang, M. Grefe, A. Trujillo-Ocampo, W. Ruiz-Vasquez, K. Takahashi, H. A. Abbas, P. Borges, D. A. Antunes, G. Al-Atrash, N. Daver, J. J. Molldrem, A. Futreal, G. Garcia-Manero, and J. S. Im, “Immunologic Predictors for Clinical Responses during Immune Checkpoint Blockade in Patients with Myelodysplastic Syndromes,” Clinical Cancer Research, vol. 29, no. 10, pp. 1938–1951, May 2023.

publisher

BibTeX

@article{lee2023-mds,
  author = "Lee, S. E. and Wang, F. and Grefe, M. and Trujillo-Ocampo, A. and Ruiz-Vasquez, W. and Takahashi, K. and Abbas, H. A. and Borges, P. and Antunes, D. A. and Al-Atrash, G. and Daver, N. and Molldrem, J. J. and Futreal, A. and Garcia-Manero, G. and Im, J. S.",
  title = "Immunologic Predictors for Clinical Responses during Immune Checkpoint Blockade in Patients with Myelodysplastic Syndromes",
  year = "2023",
  doi = "10.1158/1078-0432.CCR-22-2601",
  pmid = "36988276",
  month = may,
  volume = "29",
  number = "10",
  pages = "1938-1951",
  publisher = "American Association for Cancer Research",
  journal = "Clinical Cancer Research",
  abstract = "PURPOSE: The aim of this study is to determine immune-related biomarkers to predict effective antitumor immunity in myelodysplastic syndrome (MDS) during immunotherapy (IMT, αCTLA-4, and/or αPD-1 antibodies) and/or hypomethylating agent (HMA). EXPERIMENTAL DESIGN: Peripheral blood samples from 55 patients with MDS were assessed for immune subsets, T-cell receptor (TCR) repertoire, mutations in 295 acute myeloid leukemia (AML)/MDS-related genes, and immune-related gene expression profiling before and after the first treatment. RESULTS: Clinical responders treated with IMT ± HMA but not HMA alone showed a significant expansion of central memory (CM) CD8+ T cells, diverse TCRβ repertoire pretreatment with increased clonality and emergence of novel clones after the initial treatment, and a higher mutation burden pretreatment with subsequent reduction posttreatment. Autophagy, TGFβ, and Th1 differentiation pathways were the most downregulated in nonresponders after treatment, while upregulated in responders. Finally, CTLA-4 but not PD-1 blockade attributed to favorable changes in immune landscape. CONCLUSIONS: Analysis of tumor-immune landscape in MDS during immunotherapy provides clinical response biomarkers."
}

Abstract

PURPOSE: The aim of this study is to determine immune-related biomarkers to predict effective antitumor immunity in myelodysplastic syndrome (MDS) during immunotherapy (IMT, αCTLA-4, and/or αPD-1 antibodies) and/or hypomethylating agent (HMA). EXPERIMENTAL DESIGN: Peripheral blood samples from 55 patients with MDS were assessed for immune subsets, T-cell receptor (TCR) repertoire, mutations in 295 acute myeloid leukemia (AML)/MDS-related genes, and immune-related gene expression profiling before and after the first treatment. RESULTS: Clinical responders treated with IMT ± HMA but not HMA alone showed a significant expansion of central memory (CM) CD8+ T cells, diverse TCRβ repertoire pretreatment with increased clonality and emergence of novel clones after the initial treatment, and a higher mutation burden pretreatment with subsequent reduction posttreatment. Autophagy, TGFβ, and Th1 differentiation pathways were the most downregulated in nonresponders after treatment, while upregulated in responders. Finally, CTLA-4 but not PD-1 blockade attributed to favorable changes in immune landscape. CONCLUSIONS: Analysis of tumor-immune landscape in MDS during immunotherapy provides clinical response biomarkers.

S. Klein, J. Mischke, F. Beruldsen, I. Prinz, D. A. Antunes, M. Cornberg, and A. R. M. Kraft, “Individual Epitope-Specific CD8(+) T Cell Immune Responses Are Shaped Differently during Chronic Viral Infection,” Pathogens, vol. 12, no. 5, p. 716, May 2023.

pdf publisher

BibTeX

@article{klein2023-cd8-lcmv,
  author = "Klein, S. and Mischke, J. and Beruldsen, F. and Prinz, I. and Antunes, D. A. and Cornberg, M. and Kraft, A. R. M.",
  title = "Individual Epitope-Specific CD8(+) T Cell Immune Responses Are Shaped Differently during Chronic Viral Infection",
  year = "2023",
  doi = "10.3390/pathogens12050716",
  pmid = "37242386",
  month = may,
  volume = "12",
  number = "5",
  pages = "716",
  journal = "Pathogens",
  abstract = "A hallmark in chronic viral infections are exhausted antigen-specific CD8+ T cell responses and the inability of the immune system to eliminate the virus. Currently, there is limited information on the variability of epitope-specific T cell exhaustion within one immune response and the relevance to the T cell receptor (TCR) repertoire. The aim of this study was a comprehensive analysis and comparison of three lymphocytic choriomeningitis virus (LCMV) epitope-specific CD8+ T cell responses (NP396, GP33 and NP205) in a chronic setting with immune intervention, e.g., immune checkpoint inhibitor (ICI) therapy, in regard to the TCR repertoire. These responses, though measured within the same mice, were individual and independent from each other. The massively exhausted NP396-specific CD8+ T cells revealed a significantly reduced TCR repertoire diversity, whereas less-exhausted GP33-specific CD8+ T cell responses were rather unaffected by chronicity in regard to their TCR repertoire diversity. NP205-specific CD8+ T cell responses showed a very special TCR repertoire with a prominent public motif of TCR clonotypes that was present in all NP205-specific responses, which separated this from NP396- and GP33-specific responses. Additionally, we showed that TCR repertoire shifts induced by ICI therapy are heterogeneous on the epitope level, by revealing profound effects in NP396-, less severe and opposed effects in NP205-, and minor effects in GP33-specific responses. Overall, our data revealed individual epitope-specific responses within one viral response that are differently affected by exhaustion and ICI therapy. These individual shapings of epitope-specific T cell responses and their TCR repertoires in an LCMV mouse model indicates important implications for focusing on epitope-specific responses in future evaluations for therapeutic approaches, e.g., for chronic hepatitis virus infections in humans."
}

Abstract

A hallmark in chronic viral infections are exhausted antigen-specific CD8+ T cell responses and the inability of the immune system to eliminate the virus. Currently, there is limited information on the variability of epitope-specific T cell exhaustion within one immune response and the relevance to the T cell receptor (TCR) repertoire. The aim of this study was a comprehensive analysis and comparison of three lymphocytic choriomeningitis virus (LCMV) epitope-specific CD8+ T cell responses (NP396, GP33 and NP205) in a chronic setting with immune intervention, e.g., immune checkpoint inhibitor (ICI) therapy, in regard to the TCR repertoire. These responses, though measured within the same mice, were individual and independent from each other. The massively exhausted NP396-specific CD8+ T cells revealed a significantly reduced TCR repertoire diversity, whereas less-exhausted GP33-specific CD8+ T cell responses were rather unaffected by chronicity in regard to their TCR repertoire diversity. NP205-specific CD8+ T cell responses showed a very special TCR repertoire with a prominent public motif of TCR clonotypes that was present in all NP205-specific responses, which separated this from NP396- and GP33-specific responses. Additionally, we showed that TCR repertoire shifts induced by ICI therapy are heterogeneous on the epitope level, by revealing profound effects in NP396-, less severe and opposed effects in NP205-, and minor effects in GP33-specific responses. Overall, our data revealed individual epitope-specific responses within one viral response that are differently affected by exhaustion and ICI therapy. These individual shapings of epitope-specific T cell responses and their TCR repertoires in an LCMV mouse model indicates important implications for focusing on epitope-specific responses in future evaluations for therapeutic approaches, e.g., for chronic hepatitis virus infections in humans.

P. N. Desai, B. Wang, A. Fonseca, P. Borges, F. Z. Jelloul, P. K. Reville, E. Lee, C. Ly, A. Basi, J. Root, N. Baran, S. M. Post, Q. Deng, H. Sun, A. O. Harmanci, J. K. Burks, J. A. Gomez, C. D. DiNardo, N. G. Daver, G. Alatrash, M. Konopleva, M. R. Green, D. A. Antunes, A. Futreal, D. Hao, and H. A. Abbas, “Single-Cell Profiling of CD8+ T Cells in Acute Myeloid Leukemia Reveals a Continuous Spectrum of Differentiation and Clonal Hyperexpansion,” Cancer Immunology Research, May 2023.

publisher

BibTeX

@article{desai2023-cd8-aml,
  author = "Desai, P. N. and Wang, B. and Fonseca, A. and Borges, P. and Jelloul, F. Z. and Reville, P. K. and Lee, E. and Ly, C. and Basi, A. and Root, J. and Baran, N. and Post, S. M. and Deng, Q. and Sun, H. and Harmanci, A. O. and Burks, J. K. and Gomez, J. A. and DiNardo, C. D. and Daver, N. G. and Alatrash, G. and Konopleva, M. and Green, M. R. and Antunes, D. A. and Futreal, A. and Hao, D. and Abbas, H. A.",
  title = "Single-Cell Profiling of CD8+ T Cells in Acute Myeloid Leukemia Reveals a Continuous Spectrum of Differentiation and Clonal Hyperexpansion",
  year = "2023",
  doi = "10.1158/2326-6066.CIR-22-0961",
  pmid = "37163233",
  month = may,
  journal = "Cancer Immunology Research",
  abstract = "Comprehensive investigation of CD8+ T cells in acute myeloid leukemia (AML) is essential for developing immunotherapeutic strategies beyond immune checkpoint blockade. Herein, we performed single-cell RNA profiling of CD8+ T cells from 3 healthy bone marrow donors and 23 newly diagnosed (NewlyDx) and 8 relapsed/refractory (RelRef) AML patients. Cells co-expressing canonical exhaustion markers formed a cluster constituting <1% of all CD8+ T cells. We identified two effector CD8+ T cell subsets characterized by distinct cytokine and metabolic profiles that were differentially enriched in NewlyDx and RelRef patients. We refined a 25-gene CD8-derived signature correlating with therapy resistance, including genes associated with activation, chemoresistance, and terminal differentiation. Pseudotemporal trajectory analysis supported enrichment of a terminally differentiated state in CD8+ T cells with high CD8-derived signature expression at relapse or refractory disease. Higher expression of the 25-gene CD8 AML signature correlated with poorer outcomes in previously untreated AML patients, suggesting that the bona fide state of CD8+ T cells and their degree of differentiation are clinically relevant. Immune clonotype tracking revealed more phenotypic transitions in CD8 clonotypes in NewlyDx than in RelRef patients. Furthermore, CD8+ T cells from RelRef patients had a higher degree of clonal hyperexpansion associated with terminal differentiation and higher CD8-derived signature expression. Clonotype-derived antigen prediction revealed that most previously unreported clonotypes were patient-specific, suggesting significant heterogeneity in AML immunogenicity. Thus, immunologic reconstitution in AML is likely to be most successful at earlier disease stages when CD8+ T cells are less differentiated and have greater capacity for clonotype transitions."
}

Abstract

Comprehensive investigation of CD8+ T cells in acute myeloid leukemia (AML) is essential for developing immunotherapeutic strategies beyond immune checkpoint blockade. Herein, we performed single-cell RNA profiling of CD8+ T cells from 3 healthy bone marrow donors and 23 newly diagnosed (NewlyDx) and 8 relapsed/refractory (RelRef) AML patients. Cells co-expressing canonical exhaustion markers formed a cluster constituting <1% of all CD8+ T cells. We identified two effector CD8+ T cell subsets characterized by distinct cytokine and metabolic profiles that were differentially enriched in NewlyDx and RelRef patients. We refined a 25-gene CD8-derived signature correlating with therapy resistance, including genes associated with activation, chemoresistance, and terminal differentiation. Pseudotemporal trajectory analysis supported enrichment of a terminally differentiated state in CD8+ T cells with high CD8-derived signature expression at relapse or refractory disease. Higher expression of the 25-gene CD8 AML signature correlated with poorer outcomes in previously untreated AML patients, suggesting that the bona fide state of CD8+ T cells and their degree of differentiation are clinically relevant. Immune clonotype tracking revealed more phenotypic transitions in CD8 clonotypes in NewlyDx than in RelRef patients. Furthermore, CD8+ T cells from RelRef patients had a higher degree of clonal hyperexpansion associated with terminal differentiation and higher CD8-derived signature expression. Clonotype-derived antigen prediction revealed that most previously unreported clonotypes were patient-specific, suggesting significant heterogeneity in AML immunogenicity. Thus, immunologic reconstitution in AML is likely to be most successful at earlier disease stages when CD8+ T cells are less differentiated and have greater capacity for clonotype transitions.

H. M. Sonnemann, B. Pazdrak, D. A. Antunes, J. Roszik, and G. Lizée, “Vestigial-like 1 (VGLL1): An ancient co-transcriptional activator linking wing, placenta, and tumor development,” Biochimica et Biophysica Acta Reviews on Cancer, vol. 1878, no. 3, p. 188892, May 2023.

pdf publisher

BibTeX

@article{sonnemann2023-vgll1,
  author = "Sonnemann, H. M. and Pazdrak, B. and Antunes, D. A. and Roszik, J. and Lizée, G.",
  title = "Vestigial-like 1 (VGLL1): An ancient co-transcriptional activator linking wing, placenta, and tumor development",
  year = "2023",
  doi = "10.1016/j.bbcan.2023.188892",
  pmid = "37004960",
  month = may,
  volume = "1878",
  number = "3",
  pages = "188892",
  publisher = "Elsevier B.V.",
  journal = "Biochimica et Biophysica Acta Reviews on Cancer",
  abstract = "Vestigial-like 1 (VGLL1) is a recently discovered driver of proliferation and invasion that is expressed in many aggressive human malignancies and is strongly associated with poor prognosis. The VGLL1 gene encodes for a co-transcriptional activator that shows intriguing structural similarity to key activators in the hippo pathway, providing important clues to its functional role. VGLL1 binds to TEAD transcription factors in an analogous fashion to YAP1 but appears to activate a distinct set of downstream gene targets. In mammals, VGLL1 expression is found almost exclusively in placental trophoblasts, cells that share many hallmarks of cancer. Due to its role as a driver of tumor progression, VGLL1 has become a target of interest for potential anticancer therapies. In this review, we discuss VGLL1 from an evolutionary perspective, contrast its role in placental and tumor development, summarize the current knowledge of how signaling pathways can modulate VGLL1 function, and discuss potential approaches for targeting VGLL1 therapeutically."
}

Abstract

Vestigial-like 1 (VGLL1) is a recently discovered driver of proliferation and invasion that is expressed in many aggressive human malignancies and is strongly associated with poor prognosis. The VGLL1 gene encodes for a co-transcriptional activator that shows intriguing structural similarity to key activators in the hippo pathway, providing important clues to its functional role. VGLL1 binds to TEAD transcription factors in an analogous fashion to YAP1 but appears to activate a distinct set of downstream gene targets. In mammals, VGLL1 expression is found almost exclusively in placental trophoblasts, cells that share many hallmarks of cancer. Due to its role as a driver of tumor progression, VGLL1 has become a target of interest for potential anticancer therapies. In this review, we discuss VGLL1 from an evolutionary perspective, contrast its role in placental and tumor development, summarize the current knowledge of how signaling pathways can modulate VGLL1 function, and discuss potential approaches for targeting VGLL1 therapeutically.

S. Hall-Swan, J. Slone, M. M. Rigo, D. A. Antunes, G. Lizée, and L. E. Kavraki, “PepSim: T-cell cross-reactivity prediction via comparison of peptide sequence and peptide-HLA structure,” Frontiers in Immunology, vol. 14, p. 1108303, Apr. 2023.

pdf publisher

BibTeX

@article{hallswan2023-pepsim,
  author = "Hall-Swan, S. and Slone, J. and Rigo, M. M. and Antunes, D. A. and Lizée, G. and Kavraki, L. E.",
  title = "PepSim: T-cell cross-reactivity prediction via comparison of peptide sequence and peptide-HLA structure",
  year = "2023",
  doi = "10.3389/fimmu.2023.1108303",
  pmid = "37187737",
  month = apr,
  volume = "14",
  pages = "1108303",
  journal = "Frontiers in Immunology",
  abstract = "INTRODUCTION: Peptide-HLA class I (pHLA) complexes on the surface of tumor cells can be targeted by cytotoxic T-cells to eliminate tumors, and this is one of the bases for T-cell-based immunotherapies. However, there exist cases where therapeutic T-cells directed towards tumor pHLA complexes may also recognize pHLAs from healthy normal cells. The process where the same T-cell clone recognizes more than one pHLA is referred to as T-cell cross-reactivity and this process is driven mainly by features that make pHLAs similar to each other. T-cell cross-reactivity prediction is critical for designing T-cell-based cancer immunotherapies that are both effective and safe. METHODS: Here we present PepSim, a novel score to predict T-cell cross-reactivity based on the structural and biochemical similarity of pHLAs. RESULTS AND DISCUSSION: We show our method can accurately separate cross-reactive from non-crossreactive pHLAs in a diverse set of datasets including cancer, viral, and self-peptides. PepSim can be generalized to work on any dataset of class I peptide-HLAs and is freely available as a web server at pepsim.kavrakilab.org."
}

Abstract

INTRODUCTION: Peptide-HLA class I (pHLA) complexes on the surface of tumor cells can be targeted by cytotoxic T-cells to eliminate tumors, and this is one of the bases for T-cell-based immunotherapies. However, there exist cases where therapeutic T-cells directed towards tumor pHLA complexes may also recognize pHLAs from healthy normal cells. The process where the same T-cell clone recognizes more than one pHLA is referred to as T-cell cross-reactivity and this process is driven mainly by features that make pHLAs similar to each other. T-cell cross-reactivity prediction is critical for designing T-cell-based cancer immunotherapies that are both effective and safe. METHODS: Here we present PepSim, a novel score to predict T-cell cross-reactivity based on the structural and biochemical similarity of pHLAs. RESULTS AND DISCUSSION: We show our method can accurately separate cross-reactive from non-crossreactive pHLAs in a diverse set of datasets including cancer, viral, and self-peptides. PepSim can be generalized to work on any dataset of class I peptide-HLAs and is freely available as a web server at pepsim.kavrakilab.org.

2022

K. R. Jackson, D. A. Antunes, A. H. Talukder, A. R. Maleki, K. Amagai, A. Salmon, A. S. Katailiha, Y. Chiu, R. Fasoulis, M. M. Rigo, J. R. Abella, B. D. Melendez, F. Li, Y. Sun, H. M. Sonnemann, V. Belousov, F. Frenkel, S. Justesen, A. Makaju, Y. Liu, D. Horn, D. Lopez-Ferrer, A. F. Huhmer, P. Hwu, J. Roszik, D. Hawke, L. E. Kavraki, and G. Lizée, “Charge-based interactions through peptide position 4 drive diversity of antigen presentation by human leukocyte antigen class I molecules,” PNAS Nexus, vol. 1, no. 3, p. pgac124, July 2022.

pdf publisher

BibTeX

@article{jackson2022-pnasnexus-p4,
  author = "Jackson, K. R. and Antunes, D. A. and Talukder, A. H. and Maleki, A. R. and Amagai, K. and Salmon, A. and Katailiha, A. S. and Chiu, Y. and Fasoulis, R. and Rigo, M. M. and Abella, J. R. and Melendez, B. D. and Li, F. and Sun, Y. and Sonnemann, H. M. and Belousov, V. and Frenkel, F. and Justesen, S. and Makaju, A. and Liu, Y. and Horn, D. and Lopez-Ferrer, D. and Huhmer, A. F. and Hwu, P. and Roszik, J. and Hawke, D. and Kavraki, L. E. and Lizée, G.",
  title = "Charge-based interactions through peptide position 4 drive diversity of antigen presentation by human leukocyte antigen class I molecules",
  year = "2022",
  doi = "10.1093/pnasnexus/pgac124",
  pmid = "36003074",
  month = jul,
  volume = "1",
  number = "3",
  pages = "pgac124",
  publisher = "Oxford University Press on behalf of the National Academy of Sciences",
  journal = "PNAS Nexus",
  abstract = "Human leukocyte antigen class I (HLA-I) molecules bind and present peptides at the cell surface to facilitate the induction of appropriate CD8+ T cell-mediated immune responses to pathogen- and self-derived proteins. The HLA-I peptide-binding cleft contains dominant anchor sites in the B and F pockets that interact primarily with amino acids at peptide position 2 and the C-terminus, respectively. Nonpocket peptide-HLA interactions also contribute to peptide binding and stability, but these secondary interactions are thought to be unique to individual HLA allotypes or to specific peptide antigens. Here, we show that two positively charged residues located near the top of peptide-binding cleft facilitate interactions with negatively charged residues at position 4 of presented peptides, which occur at elevated frequencies across most HLA-I allotypes. Loss of these interactions was shown to impair HLA-I/peptide binding and complex stability, as demonstrated by both in vitro and in silico experiments. Furthermore, mutation of these Arginine-65 (R65) and/or Lysine-66 (K66) residues in HLA-A*02:01 and A*24:02 significantly reduced HLA-I cell surface expression while also reducing the diversity of the presented peptide repertoire by up to 5-fold. The impact of the R65 mutation demonstrates that nonpocket HLA-I/peptide interactions can constitute anchor motifs that exert an unexpectedly broad influence on HLA-I-mediated antigen presentation. These findings provide fundamental insights into peptide antigen binding that could broadly inform epitope discovery in the context of viral vaccine development and cancer immunotherapy."
}

Abstract

Human leukocyte antigen class I (HLA-I) molecules bind and present peptides at the cell surface to facilitate the induction of appropriate CD8+ T cell-mediated immune responses to pathogen- and self-derived proteins. The HLA-I peptide-binding cleft contains dominant anchor sites in the B and F pockets that interact primarily with amino acids at peptide position 2 and the C-terminus, respectively. Nonpocket peptide-HLA interactions also contribute to peptide binding and stability, but these secondary interactions are thought to be unique to individual HLA allotypes or to specific peptide antigens. Here, we show that two positively charged residues located near the top of peptide-binding cleft facilitate interactions with negatively charged residues at position 4 of presented peptides, which occur at elevated frequencies across most HLA-I allotypes. Loss of these interactions was shown to impair HLA-I/peptide binding and complex stability, as demonstrated by both in vitro and in silico experiments. Furthermore, mutation of these Arginine-65 (R65) and/or Lysine-66 (K66) residues in HLA-A*02:01 and A*24:02 significantly reduced HLA-I cell surface expression while also reducing the diversity of the presented peptide repertoire by up to 5-fold. The impact of the R65 mutation demonstrates that nonpocket HLA-I/peptide interactions can constitute anchor motifs that exert an unexpectedly broad influence on HLA-I-mediated antigen presentation. These findings provide fundamental insights into peptide antigen binding that could broadly inform epitope discovery in the context of viral vaccine development and cancer immunotherapy.

M. M. Rigo, R. Fasoulis, A. Conev, S. Hall-Swan, D. A. Antunes, and L. E. Kavraki, “SARS-Arena: Sequence and Structure-Guided Selection of Conserved Peptides from SARS-related Coronaviruses for Novel Vaccine Development,” Frontiers in Immunology, vol. 13, p. 931155, July 2022.

pdf publisher

BibTeX

@article{rigo2022-sars-arena,
  author = "Rigo, M. M. and Fasoulis, R. and Conev, A. and Hall-Swan, S. and Antunes, D. A. and Kavraki, L. E.",
  title = "SARS-Arena: Sequence and Structure-Guided Selection of Conserved Peptides from SARS-related Coronaviruses for Novel Vaccine Development",
  year = "2022",
  doi = "10.3389/fimmu.2022.931155",
  pmid = "35903104",
  month = jul,
  volume = "13",
  pages = "931155",
  journal = "Frontiers in Immunology",
  abstract = "The pandemic caused by the SARS-CoV-2 virus, the agent responsible for the COVID-19 disease, has affected millions of people worldwide. There is constant search for new therapies to either prevent or mitigate the disease. Fortunately, we have observed the successful development of multiple vaccines. Most of them are focused on one viral envelope protein, the spike protein. However, such focused approaches may contribute for the rise of new variants, fueled by the constant selection pressure on envelope proteins, and the widespread dispersion of coronaviruses in nature. Therefore, it is important to examine other proteins, preferentially those that are less susceptible to selection pressure, such as the nucleocapsid (N) protein. Even though the N protein is less accessible to humoral response, peptides from its conserved regions can be presented by class I Human Leukocyte Antigen (HLA) molecules, eliciting an immune response mediated by T-cells. Given the increased number of protein sequences deposited in biological databases daily and the N protein conservation among viral strains, computational methods can be leveraged to discover potential new targets for SARS-CoV-2 and SARS-CoV-related viruses. Here we developed SARS-Arena, a user-friendly computational pipeline that can be used by practitioners of different levels of expertise for novel vaccine development. SARS-Arena combines sequence-based methods and structure-based analyses to (i) perform multiple sequence alignment (MSA) of SARS-CoV-related N protein sequences, (ii) recover candidate peptides of different lengths from conserved protein regions, and (iii) model the 3D structure of the conserved peptides in the context of different HLAs. We present two main Jupyter Notebook workflows that can help in the identification of new T-cell targets against SARS-CoV viruses. In fact, in a cross-reactive case study, our workflows identified a conserved N protein peptide (SPRWYFYYL) recognized by CD8+ T-cells in the context of HLA-B7+. SARS-Arena is available at https://github.com/KavrakiLab/SARS-Arena."
}

Abstract

The pandemic caused by the SARS-CoV-2 virus, the agent responsible for the COVID-19 disease, has affected millions of people worldwide. There is constant search for new therapies to either prevent or mitigate the disease. Fortunately, we have observed the successful development of multiple vaccines. Most of them are focused on one viral envelope protein, the spike protein. However, such focused approaches may contribute for the rise of new variants, fueled by the constant selection pressure on envelope proteins, and the widespread dispersion of coronaviruses in nature. Therefore, it is important to examine other proteins, preferentially those that are less susceptible to selection pressure, such as the nucleocapsid (N) protein. Even though the N protein is less accessible to humoral response, peptides from its conserved regions can be presented by class I Human Leukocyte Antigen (HLA) molecules, eliciting an immune response mediated by T-cells. Given the increased number of protein sequences deposited in biological databases daily and the N protein conservation among viral strains, computational methods can be leveraged to discover potential new targets for SARS-CoV-2 and SARS-CoV-related viruses. Here we developed SARS-Arena, a user-friendly computational pipeline that can be used by practitioners of different levels of expertise for novel vaccine development. SARS-Arena combines sequence-based methods and structure-based analyses to (i) perform multiple sequence alignment (MSA) of SARS-CoV-related N protein sequences, (ii) recover candidate peptides of different lengths from conserved protein regions, and (iii) model the 3D structure of the conserved peptides in the context of different HLAs. We present two main Jupyter Notebook workflows that can help in the identification of new T-cell targets against SARS-CoV viruses. In fact, in a cross-reactive case study, our workflows identified a conserved N protein peptide (SPRWYFYYL) recognized by CD8+ T-cells in the context of HLA-B7+. SARS-Arena is available at https://github.com/KavrakiLab/SARS-Arena.

A. Conev, D. Devaurs, M. M. Rigo, D. A. Antunes, and L. E. Kavraki, “3pHLA-score improves structure-based peptide-HLA binding affinity prediction,” Scientific Reports, vol. 12, no. 1, p. 10749, June 2022.

pdf publisher

BibTeX

@article{conev2022-3phla,
  author = "Conev, A. and Devaurs, D. and Rigo, M. M. and Antunes, D. A. and Kavraki, L. E.",
  title = "3pHLA-score improves structure-based peptide-HLA binding affinity prediction",
  year = "2022",
  doi = "10.1038/s41598-022-14526-x",
  pmid = "35750701",
  month = jun,
  volume = "12",
  number = "1",
  pages = "10749",
  journal = "Scientific Reports",
  abstract = "Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering immune response. Estimating peptide-HLA (pHLA) binding is crucial for peptide vaccine target identification and epitope discovery pipelines. Computational methods for binding affinity prediction can accelerate these pipelines. Currently, most of those computational methods rely exclusively on sequence-based data, which leads to inherent limitations. Recent studies have shown that structure-based data can address some of these limitations. In this work we propose a novel machine learning (ML) structure-based protocol to predict binding affinity of peptides to HLA receptors. For that, we engineer the input features for ML models by decoupling energy contributions at different residue positions in peptides, which leads to our novel per-peptide-position protocol. Using Rosetta's ref2015 scoring function as a baseline we use this protocol to develop 3pHLA-score. Our per-peptide-position protocol outperforms the standard training protocol and leads to an increase from 0.82 to 0.99 of the area under the precision-recall curve. 3pHLA-score outperforms widely used scoring functions (AutoDock4, Vina, Dope, Vinardo, FoldX, GradDock) in a structural virtual screening task. Overall, this work brings structure-based methods one step closer to epitope discovery pipelines and could help advance the development of cancer and viral vaccines."
}

Abstract

Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering immune response. Estimating peptide-HLA (pHLA) binding is crucial for peptide vaccine target identification and epitope discovery pipelines. Computational methods for binding affinity prediction can accelerate these pipelines. Currently, most of those computational methods rely exclusively on sequence-based data, which leads to inherent limitations. Recent studies have shown that structure-based data can address some of these limitations. In this work we propose a novel machine learning (ML) structure-based protocol to predict binding affinity of peptides to HLA receptors. For that, we engineer the input features for ML models by decoupling energy contributions at different residue positions in peptides, which leads to our novel per-peptide-position protocol. Using Rosetta’s ref2015 scoring function as a baseline we use this protocol to develop 3pHLA-score. Our per-peptide-position protocol outperforms the standard training protocol and leads to an increase from 0.82 to 0.99 of the area under the precision-recall curve. 3pHLA-score outperforms widely used scoring functions (AutoDock4, Vina, Dope, Vinardo, FoldX, GradDock) in a structural virtual screening task. Overall, this work brings structure-based methods one step closer to epitope discovery pipelines and could help advance the development of cancer and viral vaccines.

N. Sapoval, A. Aghazadeh, M. G. Nute, D. A. Antunes, A. Balaji, R. Baraniuk, C. J. Barberan, R. Dannenfelser, C. Dun, M. Edrisi, R. A. L. Elworth, B. Kille, A. Kyrillidis, L. Nakhleh, C. R. Wolfe, Z. Yan, V. Yao, and T. J. Treangen, “Current progress and open challenges for applying deep learning across the biosciences,” Nature Communications, vol. 13, no. 1, p. 1728, Apr. 2022.

pdf publisher

BibTeX

@article{sapoval2022-natcomm-deep,
  author = "Sapoval, N. and Aghazadeh, A. and Nute, M. G. and Antunes, D. A. and Balaji, A. and Baraniuk, R. and Barberan, C. J. and Dannenfelser, R. and Dun, C. and Edrisi, M. and Elworth, R. A. L. and Kille, B. and Kyrillidis, A. and Nakhleh, L. and Wolfe, C. R. and Yan, Z. and Yao, V. and Treangen, T. J.",
  title = "Current progress and open challenges for applying deep learning across the biosciences",
  year = "2022",
  doi = "10.1038/s41467-022-29268-7",
  pmid = "35365602",
  month = apr,
  volume = "13",
  number = "1",
  pages = "1728",
  journal = "Nature Communications",
  abstract = "Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences."
}

Abstract

Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.

D. Devaurs, D. A. Antunes, and A. J. Borysik, “Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data,” Journal of the American Society for Mass Spectrometry, vol. 33, no. 2, pp. 215–237, Feb. 2022.

pdf publisher

BibTeX

@article{devaurs2022-jasms-hdx,
  author = "Devaurs, D. and Antunes, D. A. and Borysik, A. J.",
  title = "Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data",
  year = "2022",
  doi = "10.1021/jasms.1c00328",
  pmid = "35077179",
  month = feb,
  volume = "33",
  number = "2",
  pages = "215-237",
  journal = "Journal of the American Society for Mass Spectrometry",
  abstract = "Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches. However, hydrogen-exchange data have often only been used to qualitatively assess molecular structures produced by computational modeling tools. In this paper, we look beyond qualitative approaches and survey the various paradigms under which hydrogen-exchange data have been used to quantitatively guide the computational modeling of molecular structures. Although numerous prediction models have been proposed to link molecular structure and hydrogen exchange, none of them has been widely accepted by the structural biology community. Here, we present as many hydrogen-exchange prediction models as we could find in the literature, with the aim of providing the first exhaustive list of its kind. From purely structure-based models to so-called fractional-population models or knowledge-based models, the field is quite vast. We aspire for this paper to become a resource for practitioners to gain a broader perspective on the field and guide research toward the definition of better prediction models. This will eventually improve synergies between hydrogen-exchange monitoring and molecular modeling."
}

Abstract

Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches. However, hydrogen-exchange data have often only been used to qualitatively assess molecular structures produced by computational modeling tools. In this paper, we look beyond qualitative approaches and survey the various paradigms under which hydrogen-exchange data have been used to quantitatively guide the computational modeling of molecular structures. Although numerous prediction models have been proposed to link molecular structure and hydrogen exchange, none of them has been widely accepted by the structural biology community. Here, we present as many hydrogen-exchange prediction models as we could find in the literature, with the aim of providing the first exhaustive list of its kind. From purely structure-based models to so-called fractional-population models or knowledge-based models, the field is quite vast. We aspire for this paper to become a resource for practitioners to gain a broader perspective on the field and guide research toward the definition of better prediction models. This will eventually improve synergies between hydrogen-exchange monitoring and molecular modeling.

2021

S. Hall-Swan, D. Devaurs, M. M. Rigo, D. A. Antunes, L. E. Kavraki, and G. Zanatta, “DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins,” Comput Biol Med, vol. 139, p. 104943, Dec. 2021.

pdf publisher

BibTeX

@article{swan2021_dinc-covid,
  author = "Hall-Swan, S. and Devaurs, D. and Rigo, M. M. and Antunes, D. A. and Kavraki, L. E. and Zanatta, G.",
  title = "{{D}{I}{N}{C}-{C}{O}{V}{I}{D}: {A} webserver for ensemble docking with flexible {S}{A}{R}{S}-{C}o{V}-2 proteins}",
  pmid = "34717233",
  doi = "10.1016/j.compbiomed.2021.104943",
  url = "https://pubmed.ncbi.nlm.nih.gov/34717233/",
  journal = "Comput Biol Med",
  year = "2021",
  volume = "139",
  pages = "104943",
  month = dec,
  abstract = "An unprecedented research effort has been undertaken in response to the ongoing COVID-19 pandemic. This has included the determination of hundreds of crystallographic structures of SARS-CoV-2 proteins, and numerous virtual screening projects searching large compound libraries for potential drug inhibitors. Unfortunately, these initiatives have had very limited success in producing effective inhibitors against SARS-CoV-2 proteins. A reason might be an often overlooked factor in these computational efforts: receptor flexibility. To address this issue we have implemented a computational tool for ensemble docking with SARS-CoV-2 proteins. We have extracted representative ensembles of protein conformations from the Protein Data Bank and from in silico molecular dynamics simulations. Twelve pre-computed ensembles of SARS-CoV-2 protein conformations have now been made available for ensemble docking via a user-friendly webserver called DINC-COVID (dinc-covid.kavrakilab.org). We have validated DINC-COVID using data on tested inhibitors of two SARS-CoV-2 proteins, obtaining good correlations between docking-derived binding energies and experimentally-determined binding affinities. Some of the best results have been obtained on a dataset of large ligands resolved via room temperature crystallography, and therefore capturing alternative receptor conformations. In addition, we have shown that the ensembles available in DINC-COVID capture different ranges of receptor flexibility, and that this diversity is useful in finding alternative binding modes of ligands. Overall, our work highlights the importance of accounting for receptor flexibility in docking studies, and provides a platform for the identification of new inhibitors against SARS-CoV-2 proteins."
}

Abstract

An unprecedented research effort has been undertaken in response to the ongoing COVID-19 pandemic. This has included the determination of hundreds of crystallographic structures of SARS-CoV-2 proteins, and numerous virtual screening projects searching large compound libraries for potential drug inhibitors. Unfortunately, these initiatives have had very limited success in producing effective inhibitors against SARS-CoV-2 proteins. A reason might be an often overlooked factor in these computational efforts: receptor flexibility. To address this issue we have implemented a computational tool for ensemble docking with SARS-CoV-2 proteins. We have extracted representative ensembles of protein conformations from the Protein Data Bank and from in silico molecular dynamics simulations. Twelve pre-computed ensembles of SARS-CoV-2 protein conformations have now been made available for ensemble docking via a user-friendly webserver called DINC-COVID (dinc-covid.kavrakilab.org). We have validated DINC-COVID using data on tested inhibitors of two SARS-CoV-2 proteins, obtaining good correlations between docking-derived binding energies and experimentally-determined binding affinities. Some of the best results have been obtained on a dataset of large ligands resolved via room temperature crystallography, and therefore capturing alternative receptor conformations. In addition, we have shown that the ensembles available in DINC-COVID capture different ranges of receptor flexibility, and that this diversity is useful in finding alternative binding modes of ligands. Overall, our work highlights the importance of accounting for receptor flexibility in docking studies, and provides a platform for the identification of new inhibitors against SARS-CoV-2 proteins.

2020

J. R. Abella, D. A. Antunes, K. Jackson, G. Lizée, C. Clementi, and L. E. Kavraki, “Markov state modeling reveals alternative unbinding pathways for peptide–MHC complexes,” Proceedings of the National Academy of Sciences, vol. 117, no. 48, pp. 30610–30618, Dec. 2020.

pdf publisher

BibTeX

@article{abella2020-pnas,
  author = "Abella, Jayvee R. and Antunes, Dinler A. and Jackson, Kyle and Liz{\'e}e, Gregory and Clementi, Cecilia and Kavraki, Lydia E.",
  title = "Markov state modeling reveals alternative unbinding pathways for peptide{\textendash}MHC complexes",
  year = "2020",
  doi = "10.1073/pnas.2007246117",
  pmid = "33184174",
  month = dec,
  volume = "117",
  number = "48",
  pages = "30610--30618",
  publisher = "National Academy of Sciences",
  abstract = "Peptide binding to MHC receptors is part of a central biological process that enables our immune system to attack diseased cells. We use molecular simulations to illuminate the mechanisms driving stable peptide{\textendash}MHC binding. Our simulation framework produces an atomistic model of the unbinding dynamics for a given peptide{\textendash}MHC, which quantifies transitions between the major states of the system (bound, intermediate, and unbound). We applied this framework to study the binding of a SARS-CoV peptide to the HLA-A*24:02 receptor. This work revealed the unexpected importance of peptide{\textquoteright}s position 4 in driving the stability of the complex, a finding with broader biomedical implications. Our methods can be applied to other peptide{\textendash}MHC complexes, requiring only a 3D model as input.Peptide binding to major histocompatibility complexes (MHCs) is a central component of the immune system, and understanding the mechanism behind stable peptide{\textendash}MHC binding will aid the development of immunotherapies. While MHC binding is mostly influenced by the identity of the so-called anchor positions of the peptide, secondary interactions from nonanchor positions are known to play a role in complex stability. However, current MHC-binding prediction methods lack an analysis of the major conformational states and might underestimate the impact of secondary interactions. In this work, we present an atomically detailed analysis of peptide{\textendash}MHC binding that can reveal the contributions of any interaction toward stability. We propose a simulation framework that uses both umbrella sampling and adaptive sampling to generate a Markov state model (MSM) for a coronavirus-derived peptide (QFKDNVILL), bound to one of the most prevalent MHC receptors in humans (HLA-A24:02). While our model reaffirms the importance of the anchor positions of the peptide in establishing stable interactions, our model also reveals the underestimated importance of position 4 (p4), a nonanchor position. We confirmed our results by simulating the impact of specific peptide mutations and validated these predictions through competitive binding assays. By comparing the MSM of the wild-type system with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways. The analysis presented here can be applied to any peptide{\textendash}MHC complex of interest with a structural model as input, representing an important step toward comprehensive modeling of the MHC class I pathway.Code for umbrella sampling, adaptive sampling, and MSM analysis, as well as representative structures, can be found in Github at https://github.com/KavrakiLab/adaptive-sampling-pmhc. Simulation data are available upon request.",
  issn = "0027-8424",
  url = "https://www.pnas.org/content/early/2020/11/10/2007246117",
  eprint = "https://www.pnas.org/content/early/2020/11/10/2007246117.full.pdf",
  journal = "Proceedings of the National Academy of Sciences"
}

Abstract

Peptide binding to MHC receptors is part of a central biological process that enables our immune system to attack diseased cells. We use molecular simulations to illuminate the mechanisms driving stable peptide–MHC binding. Our simulation framework produces an atomistic model of the unbinding dynamics for a given peptide–MHC, which quantifies transitions between the major states of the system (bound, intermediate, and unbound). We applied this framework to study the binding of a SARS-CoV peptide to the HLA-A*24:02 receptor. This work revealed the unexpected importance of peptide’s position 4 in driving the stability of the complex, a finding with broader biomedical implications. Our methods can be applied to other peptide–MHC complexes, requiring only a 3D model as input.Peptide binding to major histocompatibility complexes (MHCs) is a central component of the immune system, and understanding the mechanism behind stable peptide–MHC binding will aid the development of immunotherapies. While MHC binding is mostly influenced by the identity of the so-called anchor positions of the peptide, secondary interactions from nonanchor positions are known to play a role in complex stability. However, current MHC-binding prediction methods lack an analysis of the major conformational states and might underestimate the impact of secondary interactions. In this work, we present an atomically detailed analysis of peptide–MHC binding that can reveal the contributions of any interaction toward stability. We propose a simulation framework that uses both umbrella sampling and adaptive sampling to generate a Markov state model (MSM) for a coronavirus-derived peptide (QFKDNVILL), bound to one of the most prevalent MHC receptors in humans (HLA-A24:02). While our model reaffirms the importance of the anchor positions of the peptide in establishing stable interactions, our model also reveals the underestimated importance of position 4 (p4), a nonanchor position. We confirmed our results by simulating the impact of specific peptide mutations and validated these predictions through competitive binding assays. By comparing the MSM of the wild-type system with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways. The analysis presented here can be applied to any peptide–MHC complex of interest with a structural model as input, representing an important step toward comprehensive modeling of the MHC class I pathway.Code for umbrella sampling, adaptive sampling, and MSM analysis, as well as representative structures, can be found in Github at https://github.com/KavrakiLab/adaptive-sampling-pmhc. Simulation data are available upon request.

D. Devaurs, D. A. Antunes, and L. E. Kavraki, “Computational analysis of complement inhibitor compstatin using molecular dynamics,” J Mol Model, vol. 26, no. 9, p. 231, Aug. 2020.

pdf publisher

BibTeX

@article{devaurs2020-compstatin,
  pmid = "32789582",
  doi = "10.1007/s00894-020-04472-8",
  author = "Devaurs, D. and Antunes, D. A. and Kavraki, L. E.",
  title = "{{C}omputational analysis of complement inhibitor compstatin using molecular dynamics}",
  journal = "J Mol Model",
  year = "2020",
  volume = "26",
  number = "9",
  pages = "231",
  month = aug,
  abstract = "The complement system plays a major role in human immunity, but its abnormal activation can have severe pathological impacts. By mimicking a natural mechanism of complement regulation, the small
  peptide compstatin has proven to be a very promising complement inhibitor. Over the years, several compstatin analogs have been created, with an improved inhibitory potency. A recent analog is being developed as a candidate drug against several pathological conditions, including COVID-19. However, the reasons behind its higher potency and increased binding affinity to complement proteins are not fully clear. This computational study highlights mechanistic properties of several compstatin analogs, thus complementing previous experimental studies. We perform molecular dynamics simulations involving six analogs alone in solution and two complexes with compstatin bound to complement component 3. These simulations reveal that all the analogs we consider, except the original compstatin, naturally adopt a pre-bound conformation in solution. Interestingly, this set of analogs adopting a pre-bound conformation includes analogs that were not known to benefit from this behavior. We also show that the most recent compstatin analog (among those we consider) forms a stronger hydrogen bond network with its complement receptor than an earlier analog."
}

Abstract

The complement system plays a major role in human immunity, but its abnormal activation can have severe pathological impacts. By mimicking a natural mechanism of complement regulation, the small peptide compstatin has proven to be a very promising complement inhibitor. Over the years, several compstatin analogs have been created, with an improved inhibitory potency. A recent analog is being developed as a candidate drug against several pathological conditions, including COVID-19. However, the reasons behind its higher potency and increased binding affinity to complement proteins are not fully clear. This computational study highlights mechanistic properties of several compstatin analogs, thus complementing previous experimental studies. We perform molecular dynamics simulations involving six analogs alone in solution and two complexes with compstatin bound to complement component 3. These simulations reveal that all the analogs we consider, except the original compstatin, naturally adopt a pre-bound conformation in solution. Interestingly, this set of analogs adopting a pre-bound conformation includes analogs that were not known to benefit from this behavior. We also show that the most recent compstatin analog (among those we consider) forms a stronger hydrogen bond network with its complement receptor than an earlier analog.

D. A. Antunes, J. R. Abella, S. Hall-Swan, D. Devaurs, A. Conev, M. Moll, G. Lizée, and L. E. Kavraki, “HLA-Arena: a customizable environment for the structural modeling and analysis of peptide-HLA complexes for cancer immunotherapy,” JCO Clinical Cancer Informatics, vol. 4, pp. 623–636, July 2020.

pdf publisher

BibTeX

@article{antunes2020-hla-arena,
  pmid = "32667823",
  doi = "10.1200/CCI.19.00123",
  author = "Antunes, D. A. and Abella, J. R. and Hall-Swan, S. and Devaurs, D. and Conev, A. and Moll, M. and Liz\'{e}e, G. and Kavraki, L. E.",
  title = "{HLA}-{A}rena: a customizable environment for the structural modeling and analysis of peptide-{HLA} complexes for cancer immunotherapy",
  journal = "JCO Clinical Cancer Informatics",
  year = "2020",
  volume = "4",
  pages = "623--636",
  keywords = "fundamentals of protein modeling, proteins and drugs, other biomedical computing",
  month = jul,
  abstract = "PURPOSE: HLA protein receptors play a key role in cellular immunity. They bind intracellular peptides and display them for recognition by T-cell lymphocytes. Because T-cell activation is partially driven by structural features of these peptide-HLA complexes, their structural modeling and analysis are becoming central components of cancer immunotherapy projects. Unfortunately, this kind of analysis is limited by the small number of experimentally determined structures of peptide-HLA complexes. Overcoming this limitation requires developing novel computational methods to model and analyze peptide-HLA structures.
  
  METHODS: Here we describe a new platform for the structural modeling and analysis of peptide-HLA complexes, called HLA-Arena, which we have implemented using Jupyter Notebook and Docker. It is a customizable environment that facilitates the use of computational tools, such as APE-Gen and DINC, which we have previously applied to peptide-HLA complexes. By integrating other commonly used tools, such as MODELLER and MHCflurry, this environment includes support for diverse tasks in structural modeling, analysis, and visualization.
  
  RESULTS To illustrate the capabilities of HLA-Arena, we describe 3 example workflows applied to peptide-HLA complexes. Leveraging the strengths of our tools, DINC and APE-Gen, the first 2 workflows show how to perform geometry prediction for peptide-HLA complexes and structure-based binding prediction, respectively. The third workflow presents an example of large-scale virtual screening of peptides for multiple HLA alleles.
  
  CONCLUSION: These workflows illustrate the potential benefits of HLA-Arena for the structural modeling and analysis of peptide-HLA complexes. Because HLA-Arena can easily be integrated within larger computational pipelines, we expect its potential impact to vastly increase. For instance, it could be used to conduct structural analyses for personalized cancer immunotherapy, neoantigen discovery, or vaccine development."
}

Abstract

PURPOSE: HLA protein receptors play a key role in cellular immunity. They bind intracellular peptides and display them for recognition by T-cell lymphocytes. Because T-cell activation is partially driven by structural features of these peptide-HLA complexes, their structural modeling and analysis are becoming central components of cancer immunotherapy projects. Unfortunately, this kind of analysis is limited by the small number of experimentally determined structures of peptide-HLA complexes. Overcoming this limitation requires developing novel computational methods to model and analyze peptide-HLA structures. METHODS: Here we describe a new platform for the structural modeling and analysis of peptide-HLA complexes, called HLA-Arena, which we have implemented using Jupyter Notebook and Docker. It is a customizable environment that facilitates the use of computational tools, such as APE-Gen and DINC, which we have previously applied to peptide-HLA complexes. By integrating other commonly used tools, such as MODELLER and MHCflurry, this environment includes support for diverse tasks in structural modeling, analysis, and visualization. RESULTS To illustrate the capabilities of HLA-Arena, we describe 3 example workflows applied to peptide-HLA complexes. Leveraging the strengths of our tools, DINC and APE-Gen, the first 2 workflows show how to perform geometry prediction for peptide-HLA complexes and structure-based binding prediction, respectively. The third workflow presents an example of large-scale virtual screening of peptides for multiple HLA alleles. CONCLUSION: These workflows illustrate the potential benefits of HLA-Arena for the structural modeling and analysis of peptide-HLA complexes. Because HLA-Arena can easily be integrated within larger computational pipelines, we expect its potential impact to vastly increase. For instance, it could be used to conduct structural analyses for personalized cancer immunotherapy, neoantigen discovery, or vaccine development.

J. R. Abella, D. A. Antunes, C. Clementi, and L. E. Kavraki, “Large-scale structure-based prediction of stable peptide binding to Class I HLAs using random forests,” Frontiers in Immunology, vol. 11, no. 1583, July 2020.

pdf publisher

BibTeX

@article{abella2020-frontiers-random-forest,
  doi = "10.3389/fimmu.2020.01583",
  author = "Abella, J. R. and Antunes, D. A. and Clementi, C. and Kavraki, L. E.",
  title = "Large-scale structure-based prediction of stable peptide binding to {Class I HLAs} using random forests",
  keywords = "fundamentals of protein modeling, proteins and drugs, other biomedical computing",
  journal = "Frontiers in Immunology",
  year = "2020",
  volume = "11",
  number = "1583",
  month = jul,
  abstract = "Prediction of stable peptide binding to Class I HLAs is an important component for designing immunotherapies. While the best performing predictors are based on machine learning algorithms trained on peptide-HLA (pHLA) sequences, the use of structure for training predictors deserves further exploration. Given enough pHLA structures, a predictor based on the residue-residue interactions found in these structures has the potential to generalize for alleles with little or no experimental data. We have previously developed APE-Gen, a modeling approach able to produce pHLA structures in a scalable manner. In this work we use APE-Gen to model over 150,000 pHLA structures, the largest dataset of its kind, which were used to train a structure-based pan-allele model. We extract simple, homogenous features based on residue-residue distances between peptide and HLA, and build a random forest model for predicting stable pHLA binding. Our model achieves competitive AUROC values on leave-one-allele-out validation tests using significantly less data when compared to popular sequence-based methods. Additionally, our model offers an interpretation analysis that can reveal how the model composes the features to arrive at any given prediction. This interpretation analysis can be used to check if the model is in line with chemical intuition, and we showcase particular examples. Our work is a significant step toward using structure to achieve generalizable and more interpretable prediction for stable pHLA binding."
}

Abstract

Prediction of stable peptide binding to Class I HLAs is an important component for designing immunotherapies. While the best performing predictors are based on machine learning algorithms trained on peptide-HLA (pHLA) sequences, the use of structure for training predictors deserves further exploration. Given enough pHLA structures, a predictor based on the residue-residue interactions found in these structures has the potential to generalize for alleles with little or no experimental data. We have previously developed APE-Gen, a modeling approach able to produce pHLA structures in a scalable manner. In this work we use APE-Gen to model over 150,000 pHLA structures, the largest dataset of its kind, which were used to train a structure-based pan-allele model. We extract simple, homogenous features based on residue-residue distances between peptide and HLA, and build a random forest model for predicting stable pHLA binding. Our model achieves competitive AUROC values on leave-one-allele-out validation tests using significantly less data when compared to popular sequence-based methods. Additionally, our model offers an interpretation analysis that can reveal how the model composes the features to arrive at any given prediction. This interpretation analysis can be used to check if the model is in line with chemical intuition, and we showcase particular examples. Our work is a significant step toward using structure to achieve generalizable and more interpretable prediction for stable pHLA binding.

T. Arns, D. A. Antunes, J. R. Abella, M. M. Rigo, L. E. Kavraki, S. Giuliatti, and E. A. Donadi, “Structural Modeling and Molecular Dynamics of the Immune Checkpoint Molecule HLA-G,” Frontiers in Immunology, vol. 11, p. 2882, 2020.

pdf publisher

BibTeX

@article{arns2020,
  author = "Arns, Thais and Antunes, Dinler A. and Abella, Jayvee R. and Rigo, Maurício M. and Kavraki, Lydia E. and Giuliatti, Silvana and Donadi, Eduardo A.",
  title = "Structural Modeling and Molecular Dynamics of the Immune Checkpoint Molecule HLA-G",
  journal = "Frontiers in Immunology",
  volume = "11",
  pages = "2882",
  year = "2020",
  url = "https://www.frontiersin.org/article/10.3389/fimmu.2020.575076",
  doi = "10.3389/fimmu.2020.575076",
  issn = "1664-3224",
  abstract = "HLA-G is considered to be an immune checkpoint molecule, a function that is closely linked to the structure and dynamics of the different HLA-G isoforms. Unfortunately, little is known about the structure and dynamics of these isoforms. For instance, there are only seven crystal structures of HLA-G molecules, being all related to a single isoform, and in some cases lacking important residues associated to the interaction with leukocyte receptors. In addition, they lack information on the dynamics of both membrane-bound HLA-G forms, and soluble forms. We took advantage of in silico strategies to disclose the dynamic behavior of selected HLA-G forms, including the membrane-bound HLA-G1 molecule, soluble HLA-G1 dimer, and HLA-G5 isoform. Both the membrane-bound HLA-G1 molecule and the soluble HLA-G1 dimer were quite stable. Residues involved in the interaction with ILT2 and ILT4 receptors (α3 domain) were very close to the lipid bilayer in the complete HLA-G1 molecule, which might limit accessibility. On the other hand, these residues can be completely exposed in the soluble HLA-G1 dimer, due to the free rotation of the disulfide bridge (Cys42/Cys42). In fact, we speculate that this free rotation of each protomer (i.e., the chains composing the dimer) could enable alternative binding modes for ILT2/ILT4 receptors, which in turn could be associated with greater affinity of the soluble HLA-G1 dimer. Structural analysis of the HLA-G5 isoform demonstrated higher stability for the complex containing the peptide and coupled β2-microglobulin, while structures lacking such domains were significantly unstable. This study reports for the first time structural conformations for the HLA-G5 isoform and the dynamic behavior of HLA-G1 molecules under simulated biological conditions. All modeled structures were made available through GitHub (https://github.com/KavrakiLab/), enabling their use as templates for modeling other alleles and isoforms, as well as for other computational analyses to investigate key molecular interactions."
}

Abstract

HLA-G is considered to be an immune checkpoint molecule, a function that is closely linked to the structure and dynamics of the different HLA-G isoforms. Unfortunately, little is known about the structure and dynamics of these isoforms. For instance, there are only seven crystal structures of HLA-G molecules, being all related to a single isoform, and in some cases lacking important residues associated to the interaction with leukocyte receptors. In addition, they lack information on the dynamics of both membrane-bound HLA-G forms, and soluble forms. We took advantage of in silico strategies to disclose the dynamic behavior of selected HLA-G forms, including the membrane-bound HLA-G1 molecule, soluble HLA-G1 dimer, and HLA-G5 isoform. Both the membrane-bound HLA-G1 molecule and the soluble HLA-G1 dimer were quite stable. Residues involved in the interaction with ILT2 and ILT4 receptors (α3 domain) were very close to the lipid bilayer in the complete HLA-G1 molecule, which might limit accessibility. On the other hand, these residues can be completely exposed in the soluble HLA-G1 dimer, due to the free rotation of the disulfide bridge (Cys42/Cys42). In fact, we speculate that this free rotation of each protomer (i.e., the chains composing the dimer) could enable alternative binding modes for ILT2/ILT4 receptors, which in turn could be associated with greater affinity of the soluble HLA-G1 dimer. Structural analysis of the HLA-G5 isoform demonstrated higher stability for the complex containing the peptide and coupled β2-microglobulin, while structures lacking such domains were significantly unstable. This study reports for the first time structural conformations for the HLA-G5 isoform and the dynamic behavior of HLA-G1 molecules under simulated biological conditions. All modeled structures were made available through GitHub (https://github.com/KavrakiLab/), enabling their use as templates for modeling other alleles and isoforms, as well as for other computational analyses to investigate key molecular interactions.

2019

D. Devaurs, D. A. Antunes, S. Hall-Swan, N. Mitchell, M. Moll, G. Lizée, and L. E. Kavraki, “Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins,” BMC Molecular and Cell Biology, vol. 20, no. 1, p. 42, Sept. 2019.

pdf publisher

BibTeX

@article{devaurs2019using-parallelized-incremental-meta-docking,
  grants = "NIH 1R21CA209941",
  author = "Devaurs, Didier and Antunes, Dinler A and Hall-Swan, Sarah and Mitchell, Nicole and Moll, Mark and Liz{\'e}e, Gregory and Kavraki, Lydia E",
  journal = "BMC Molecular and Cell Biology",
  title = "Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins",
  volume = "20",
  number = "1",
  pages = "42",
  month = sep,
  year = "2019",
  keywords = "proteins and drugs",
  doi = "10.1186/s12860-019-0218-z",
  abstract = "Background: Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a challenge because of its underlying combinatorial complexity. In this study, we evaluate the impact of using parallelized and incremental paradigms on the accuracy and performance of conformational sampling when docking large ligands. We use ﬁve datasets of protein-ligand complexes involving ligands that could not be accurately docked by classical protein-ligand docking tools in previous similar studies.
  
  Results: Our computational evaluation shows that simply increasing the amount of conformational sampling performed by a protein-ligand docking tool, such as Vina, by running it for longer is rarely beneﬁcial. Instead, it is more eﬃcient and advantageous to run several short instances of this docking tool in parallel and group their results together, in a straightforward parallelized docking protocol. Even greater accuracy and eﬃciency are achieved by our parallelized incremental meta-docking tool, DINC, showing the additional beneﬁts of its incremental paradigm. Using DINC, we could accurately reproduce the vast majority of the protein-ligand complexes we considered.
  
  Conclusions: Our study suggests that, even when trying to dock large ligands to proteins, the conformational sampling of the ligand should no longer be considered an issue, as simple docking protocols using existing tools can solve it. Therefore, scoring should currently be regarded as the biggest unmet challenge in molecular docking.
  
  Keywords: molecular docking; protein-ligand docking; protein-peptide docking; conformational sampling; scoring; parallelism; incremental protocol"
}

Abstract

Background: Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a challenge because of its underlying combinatorial complexity. In this study, we evaluate the impact of using parallelized and incremental paradigms on the accuracy and performance of conformational sampling when docking large ligands. We use ﬁve datasets of protein-ligand complexes involving ligands that could not be accurately docked by classical protein-ligand docking tools in previous similar studies. Results: Our computational evaluation shows that simply increasing the amount of conformational sampling performed by a protein-ligand docking tool, such as Vina, by running it for longer is rarely beneﬁcial. Instead, it is more eﬃcient and advantageous to run several short instances of this docking tool in parallel and group their results together, in a straightforward parallelized docking protocol. Even greater accuracy and eﬃciency are achieved by our parallelized incremental meta-docking tool, DINC, showing the additional beneﬁts of its incremental paradigm. Using DINC, we could accurately reproduce the vast majority of the protein-ligand complexes we considered. Conclusions: Our study suggests that, even when trying to dock large ligands to proteins, the conformational sampling of the ligand should no longer be considered an issue, as simple docking protocols using existing tools can solve it. Therefore, scoring should currently be regarded as the biggest unmet challenge in molecular docking. Keywords: molecular docking; protein-ligand docking; protein-peptide docking; conformational sampling; scoring; parallelism; incremental protocol

C. F. Soon, S. Zhang, P. V. Suneetha, D. A. Antunes, M. P. Manns, S. Raha, C. Schultze-Florey, I. Prinz, H. Wedemeyer, M. S. Chen, and M. Cornberg, “Hepatitis E Virus (HEV)-Specific T Cell Receptor Cross-Recognition: Implications for Immunotherapy,” Frontiers in Immunology, vol. 10, p. 2076, 2019.

pdf publisher

BibTeX

@article{soon2019-hev-immunotherapy,
  pmid = "31552033",
  doi = "10.3389/fimmu.2019.02076",
  author = "Soon, Chai Fen and Zhang, Shihong and Suneetha, Pothakamuri Venkata and Antunes, Dinler Amaral and Manns, Michael Peter and Raha, Solaiman and Schultze-Florey, Christian and Prinz, Immo and Wedemeyer, Heiner and Chen, Margaret Sällberg and Cornberg, Markus",
  title = "{Hepatitis E Virus (HEV)-Specific T Cell Receptor Cross-Recognition: Implications for Immunotherapy}",
  journal = "Frontiers in Immunology",
  year = "2019",
  volume = "10",
  pages = "2076",
  keywords = "cross-reactivity, immunotherapy, viral immunity",
  abstract = "T cell immunotherapy is a concept developed for the treatment of cancer and infectious diseases, based on cytotoxic T lymphocytes to target tumor- or pathogen-specific antigens. Antigen-specificity of the T cell receptors (TCRs) is an important selection criterion in the developmental design of immunotherapy. However, off-target specificity is a possible autoimmunity concern if the engineered antigen-specific T cells are cross-reacting to self-peptides in-vivo. In our recent work, we identified several hepatitis E virus (HEV)-specific TCRs as potential candidates to be developed into T cell therapy to treat chronic hepatitis E. One of the identified TCRs, targeting a HLA-A2-restricted epitope at the RNA-dependent RNA polymerase (HEV-1527: LLWNTVWNM), possessed a unique multiple glycine motif in the TCR-β CDR3, which might be a factor inducing cross-reactivity. The aim of our study was to explore if this TCR could cross-recognize self-peptides to underlay autoimmunity. Indeed, we found that this HEV-1527-specific TCR could also cross-recognize an apoptosis-related epitope, Nonmuscle Myosin Heavy Chain 9 (MYH9-478: QLFNHTMFI). While this TCR had dual specificities to both viral epitope and a self-antigen by double Dextramer binding, it was selectively functional against HEV-1527 but not activated against MYH9-478. The consecutive glycine motif in β chain may be the reason promoting TCR binding promiscuity to recognize a secondary target, thereby facilitating cross-recognition. In conclusion, candidate TCRs for immunotherapy development should be screened for autoimmune potential, especially when the TCRs exhibit unique sequence pattern."
}

Abstract

T cell immunotherapy is a concept developed for the treatment of cancer and infectious diseases, based on cytotoxic T lymphocytes to target tumor- or pathogen-specific antigens. Antigen-specificity of the T cell receptors (TCRs) is an important selection criterion in the developmental design of immunotherapy. However, off-target specificity is a possible autoimmunity concern if the engineered antigen-specific T cells are cross-reacting to self-peptides in-vivo. In our recent work, we identified several hepatitis E virus (HEV)-specific TCRs as potential candidates to be developed into T cell therapy to treat chronic hepatitis E. One of the identified TCRs, targeting a HLA-A2-restricted epitope at the RNA-dependent RNA polymerase (HEV-1527: LLWNTVWNM), possessed a unique multiple glycine motif in the TCR-β CDR3, which might be a factor inducing cross-reactivity. The aim of our study was to explore if this TCR could cross-recognize self-peptides to underlay autoimmunity. Indeed, we found that this HEV-1527-specific TCR could also cross-recognize an apoptosis-related epitope, Nonmuscle Myosin Heavy Chain 9 (MYH9-478: QLFNHTMFI). While this TCR had dual specificities to both viral epitope and a self-antigen by double Dextramer binding, it was selectively functional against HEV-1527 but not activated against MYH9-478. The consecutive glycine motif in β chain may be the reason promoting TCR binding promiscuity to recognize a secondary target, thereby facilitating cross-recognition. In conclusion, candidate TCRs for immunotherapy development should be screened for autoimmune potential, especially when the TCRs exhibit unique sequence pattern.

J. R. Abella, D. A. Antunes, C. Clementi, and L. E. Kavraki, “APE-Gen: A Fast Method for Generating Ensembles of Bound Peptide-MHC Conformations,” Molecules, vol. 24, no. 5, 2019.

pdf publisher

BibTeX

@article{abella2019-apegen,
  author = "Abella, Jayvee R. and Antunes, Dinler A. and Clementi, Cecilia and Kavraki, Lydia E.",
  title = "APE-Gen: A Fast Method for Generating Ensembles of Bound Peptide-MHC Conformations",
  journal = "Molecules",
  volume = "24",
  year = "2019",
  number = "5",
  article-number = "881",
  url = "http://www.mdpi.com/1420-3049/24/5/881",
  issn = "1420-3049",
  abstract = "The Class I Major Histocompatibility Complex (MHC) is a central protein in immunology as it binds to intracellular peptides and displays them at the cell surface for recognition by T-cells. The structural analysis of bound peptide-MHC complexes (pMHCs) holds the promise of interpretable and general binding prediction (i.e., testing whether a given peptide binds to a given MHC). However, structural analysis is limited in part by the difficulty in modelling pMHCs given the size and flexibility of the peptides that can be presented by MHCs. This article describes APE-Gen (Anchored Peptide-MHC Ensemble Generator), a fast method for generating ensembles of bound pMHC conformations. APE-Gen generates an ensemble of bound conformations by iterated rounds of (i) anchoring the ends of a given peptide near known pockets in the binding site of the MHC, (ii) sampling peptide backbone conformations with loop modelling, and then (iii) performing energy minimization to fix steric clashes, accumulating conformations at each round. APE-Gen takes only minutes on a standard desktop to generate tens of bound conformations, and we show the ability of APE-Gen to sample conformations found in X-ray crystallography even when only sequence information is used as input. APE-Gen has the potential to be useful for its scalability (i.e., modelling thousands of pMHCs or even non-canonical longer peptides) and for its use as a flexible search tool. We demonstrate an example for studying cross-reactivity.",
  doi = "10.3390/molecules24050881",
  keywords = "fundamentals of protein modeling"
}

Abstract

The Class I Major Histocompatibility Complex (MHC) is a central protein in immunology as it binds to intracellular peptides and displays them at the cell surface for recognition by T-cells. The structural analysis of bound peptide-MHC complexes (pMHCs) holds the promise of interpretable and general binding prediction (i.e., testing whether a given peptide binds to a given MHC). However, structural analysis is limited in part by the difficulty in modelling pMHCs given the size and flexibility of the peptides that can be presented by MHCs. This article describes APE-Gen (Anchored Peptide-MHC Ensemble Generator), a fast method for generating ensembles of bound pMHC conformations. APE-Gen generates an ensemble of bound conformations by iterated rounds of (i) anchoring the ends of a given peptide near known pockets in the binding site of the MHC, (ii) sampling peptide backbone conformations with loop modelling, and then (iii) performing energy minimization to fix steric clashes, accumulating conformations at each round. APE-Gen takes only minutes on a standard desktop to generate tens of bound conformations, and we show the ability of APE-Gen to sample conformations found in X-ray crystallography even when only sequence information is used as input. APE-Gen has the potential to be useful for its scalability (i.e., modelling thousands of pMHCs or even non-canonical longer peptides) and for its use as a flexible search tool. We demonstrate an example for studying cross-reactivity.

2018

D. A. Antunes, J. R. Abella, D. Devaurs, M. M. Rigo, and L. E. Kavraki, “Structure-based methods for binding mode and binding affinity prediction for peptide-MHC complexes,” Current Topics in Medicinal Chemistry, vol. 19, no. 1, 2018.

pdf publisher

BibTeX

@article{antunes2018structure-based-methods-for-binding,
  grants = "NIH 1R21CA209941",
  abstract = "Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence-based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.",
  author = "Antunes, Dinler A. and Abella, Jayvee R. and Devaurs, Didier and Rigo, Maur\'{i}cio M. and Kavraki, Lydia E.",
  title = "Structure-based methods for binding mode and binding affinity prediction for peptide-{MHC} complexes",
  journal = "Current Topics in Medicinal Chemistry",
  year = "2018",
  volume = "19",
  number = "1",
  doi = "10.2174/1568026619666181224101744",
  keywords = "proteins and drugs, molecular docking, binding mode prediction, binding affinity prediction, peptide-MHC complexes, immunogenicity, T-cell activation",
  pmid = "30582480"
}

Abstract

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence-based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

D. Devaurs, D. A. Antunes, and L. E. Kavraki, “Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data,” International Journal of Molecular Sciences, vol. 19, no. 11, p. 3406, 2018.

pdf publisher

BibTeX

@article{devaurs2018revealing-unknown-protein0structures,
  grants = "NSF AF 1423304",
  abstract = "Both experimental and computational methods are available to gather information about a protein’s conformational space and interpret changes in protein structure. However, experimentally observing and computationally modeling large proteins remain critical challenges for structural biology. Our work aims at addressing these challenges by combining computational and experimental techniques relying on each other to overcome their respective limitations. Indeed, despite its advantages, an experimental technique such as hydrogen-exchange monitoring cannot produce structural models because of its low resolution. Additionally, the computational methods that can generate such models suffer from the curse of dimensionality when applied to large proteins. Adopting a common solution to this issue, we have recently proposed a framework in which our computational method for protein conformational sampling is biased by experimental hydrogen-exchange data. In this paper, we present our latest application of this computational framework: generating an atomic-resolution structural model for an unknown protein state. For that, starting from an available protein structure, we explore the conformational space of this protein, using hydrogen-exchange data on this unknown state as a guide. We have successfully used our computational framework to generate models for three proteins of increasing size, the biggest one undergoing large-scale conformational changes.",
  author = "Devaurs, Didier and Antunes, Dinler A. and Kavraki, Lydia E.",
  title = "Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data",
  journal = "International Journal of Molecular Sciences",
  year = "2018",
  volume = "19",
  number = "11",
  pages = "3406",
  doi = "10.3390/ijms19113406",
  keywords = "fundamentals of protein modeling"
}

Abstract

Both experimental and computational methods are available to gather information about a protein’s conformational space and interpret changes in protein structure. However, experimentally observing and computationally modeling large proteins remain critical challenges for structural biology. Our work aims at addressing these challenges by combining computational and experimental techniques relying on each other to overcome their respective limitations. Indeed, despite its advantages, an experimental technique such as hydrogen-exchange monitoring cannot produce structural models because of its low resolution. Additionally, the computational methods that can generate such models suffer from the curse of dimensionality when applied to large proteins. Adopting a common solution to this issue, we have recently proposed a framework in which our computational method for protein conformational sampling is biased by experimental hydrogen-exchange data. In this paper, we present our latest application of this computational framework: generating an atomic-resolution structural model for an unknown protein state. For that, starting from an available protein structure, we explore the conformational space of this protein, using hydrogen-exchange data on this unknown state as a guide. We have successfully used our computational framework to generate models for three proteins of increasing size, the biggest one undergoing large-scale conformational changes.

D. A. Antunes, D. Devaurs, M. Moll, G. Lizée, and L. E. Kavraki, “General prediction of peptide-MHC binding modes using incremental docking: A proof of concept,” Scientific Reports - Nature, vol. 8, p. 4327, 2018.

pdf publisher

BibTeX

@article{antunes2018-dinc-hla-proof,
  grants = "NIH 1R21CA209941",
  abstract = "The class I major histocompatibility complex (MHC) is capable of binding peptides derived from intracellular proteins and displaying them at the cell surface. The recognition of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity, enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies against cancer, which leverage this mechanism, can greatly benefit from structural analyses of pMHC complexes. Several attempts have been made to use molecular docking for such analyses, but pMHC structure remains too challenging for even state-of-the-art docking tools. To overcome these limitations, we describe the use of an incremental meta-docking approach for structural prediction of pMHC complexes. Previous methods applied in this context used specific constraints to reduce the complexity of this prediction problem, at the expense of generality. Our strategy makes no assumption and can potentially be used to predict binding modes for any pMHC complex. Our method has been tested in a re-docking experiment, reproducing the binding modes of 25 pMHC complexes whose crystal structures are available. This study is a proof of concept that incremental docking strategies can lead to general geometry prediction of pMHC complexes, with potential applications for immunotherapy against cancer or infectious diseases.",
  doi = "10.1038/s41598-018-22173-4",
  pmcid = "PMC5847594",
  pmid = "29531253",
  author = "Antunes, Dinler A and Devaurs, Didier and Moll, Mark and Liz\'{e}e, Gregory and Kavraki, Lydia E",
  title = "General prediction of peptide-MHC binding modes using incremental docking: A proof of concept",
  journal = "Scientific Reports - Nature",
  volume = "8",
  page = "4327",
  year = "2018",
  keywords = "proteins and drugs, peptide-docking, pHLA structure, geometry prediction, DINC, cancer immunotherapy"
}

Abstract

The class I major histocompatibility complex (MHC) is capable of binding peptides derived from intracellular proteins and displaying them at the cell surface. The recognition of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity, enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies against cancer, which leverage this mechanism, can greatly benefit from structural analyses of pMHC complexes. Several attempts have been made to use molecular docking for such analyses, but pMHC structure remains too challenging for even state-of-the-art docking tools. To overcome these limitations, we describe the use of an incremental meta-docking approach for structural prediction of pMHC complexes. Previous methods applied in this context used specific constraints to reduce the complexity of this prediction problem, at the expense of generality. Our strategy makes no assumption and can potentially be used to predict binding modes for any pMHC complex. Our method has been tested in a re-docking experiment, reproducing the binding modes of 25 pMHC complexes whose crystal structures are available. This study is a proof of concept that incremental docking strategies can lead to general geometry prediction of pMHC complexes, with potential applications for immunotherapy against cancer or infectious diseases.

2017

D. A. Antunes, M. Moll, D. Devaurs, K. R. Jackson, G. Lizée, and L. E. Kavraki, “DINC 2.0: a new protein-peptide docking webserver using an incremental approach,” Cancer Research, vol. 77, no. 21, pp. e55–57, Nov. 2017.

pdf publisher

BibTeX

@article{antunes2017-dinc2,
  grants = "NIH 1R21CA209941",
  abstract = "Molecular docking is a standard computational approach to predict binding modes of protein-ligand complexes, by exploring alternative orientations and conformations of the ligand (i.e., by exploring ligand flexibility). Docking tools are largely used for virtual screening of small drug-like molecules, but their accuracy and efficiency greatly decays for ligands with more than 10 flexible bonds. This prevents a broader use of these tools to dock larger ligands such as peptides, which are molecules of growing interest in cancer research. To overcome this limitation, our group has previously proposed a meta-docking strategy, called DINC, to predict binding modes of large ligands. By incrementally docking overlapping fragments of a ligand, DINC allowed predicting binding modes of peptide-based inhibitors of transcription factors involved in cancer. Here we describe DINC 2.0, a revamped version of the DINC webserver with enhanced capabilities and a more user-friendly interface. DINC 2.0 allows docking ligands that were previously too challenging for DINC, such as peptides with more than 25 flexible bonds. The webserver is freely accessible at \url{http://dinc.kavrakilab.org}, together with additional documentation and video tutorials. Our team will provide continuous support for this tool and is working on extending its applicability to other challenging fields, such as personalized immunotherapy against cancer.",
  keywords = "proteins and drugs, other biomedical computing",
  author = "Antunes, Dinler A and Moll, Mark and Devaurs, Didier and Jackson, KR and Liz\'{e}e, Gregory and Kavraki, Lydia E",
  title = "{DINC} 2.0: a new protein-peptide docking webserver using an incremental approach",
  journal = "Cancer Research",
  volume = "77",
  number = "21",
  pages = "e55-57",
  year = "2017",
  doi = "10.1158/0008-5472.CAN-17-0511",
  pmcid = "PMC5679007",
  pmid = "29092940",
  msid = "NIHMS898073",
  month = nov
}

Abstract

Molecular docking is a standard computational approach to predict binding modes of protein-ligand complexes, by exploring alternative orientations and conformations of the ligand (i.e., by exploring ligand flexibility). Docking tools are largely used for virtual screening of small drug-like molecules, but their accuracy and efficiency greatly decays for ligands with more than 10 flexible bonds. This prevents a broader use of these tools to dock larger ligands such as peptides, which are molecules of growing interest in cancer research. To overcome this limitation, our group has previously proposed a meta-docking strategy, called DINC, to predict binding modes of large ligands. By incrementally docking overlapping fragments of a ligand, DINC allowed predicting binding modes of peptide-based inhibitors of transcription factors involved in cancer. Here we describe DINC 2.0, a revamped version of the DINC webserver with enhanced capabilities and a more user-friendly interface. DINC 2.0 allows docking ligands that were previously too challenging for DINC, such as peptides with more than 25 flexible bonds. The webserver is freely accessible at \urlhttp://dinc.kavrakilab.org, together with additional documentation and video tutorials. Our team will provide continuous support for this tool and is working on extending its applicability to other challenging fields, such as personalized immunotherapy against cancer.

D. A. Antunes, M. M. Rigo, M. V. Freitas, M. M. FA, M. Sinigaglia, G. Lizée, L. E. Kavraki, L. K. Selin, M. Cornberg, and G. F. Vieira, “Interpreting T-cell cross-reactivity through structure: implications for TCR-based cancer immunotherapy,” Front. Immunol., vol. 8, no. 1210, 2017.

pdf publisher

BibTeX

@article{antunes2017-frontiers-immunol,
  abstract = "Immunotherapy has become one of the most promising avenues for cancer treatment, making use of the patient's own immune system to eliminate cancer cells. Clinical trials with T-cell-based immunotherapies have shown dramatic tumor regressions, being effective in multiple cancer types and for many different patients. Unfortunately, this progress was tempered by reports of serious (even fatal) side effects. Such therapies rely on the use of cytotoxic T-cell lymphocytes, an essential part of the adaptive immune system. Cytotoxic T-cells are regularly involved in surveillance and are capable of both eliminating diseased cells and generating protective immunological memory. The specificity of a given T-cell is determined through the structural interaction between the T-cell receptor (TCR) and a peptide-loaded major histocompatibility complex (MHC); i.e., an intracellular peptide–ligand displayed at the cell surface by an MHC molecule. However, a given TCR can recognize different peptide–MHC (pMHC) complexes, which can sometimes trigger an unwanted response that is referred to as T-cell cross-reactivity. This has become a major safety issue in TCR-based immunotherapies, following reports of melanoma-specific T-cells causing cytotoxic damage to healthy tissues (e.g., heart and nervous system). T-cell cross-reactivity has been extensively studied in the context of viral immunology and tissue transplantation. Growing evidence suggests that it is largely driven by structural similarities of seemingly unrelated pMHC complexes. Here, we review recent reports about the existence of pMHC ``hot-spots" for cross-reactivity and propose the existence of a TCR interaction profile (i.e., a refinement of a more general TCR footprint in which some amino acid residues are more important than others in triggering T-cell cross-reactivity). We also make use of available structural data and pMHC models to interpret previously reported cross-reactivity patterns among virus-derived peptides. Our study provides further evidence that structural analyses of pMHC complexes can be used to assess the intrinsic likelihood of cross-reactivity among peptide-targets. Furthermore, we hypothesize that some apparent inconsistencies in reported cross-reactivities, such as a preferential directionality, might also be driven by particular structural features of the targeted pMHC complex. Finally, we explain why TCR-based immunotherapy provides a special context in which meaningful T-cell cross-reactivity predictions can be made.",
  doi = "10.3389/fimmu.2017.01210",
  pmcid = "PMC5632759",
  pmid = "29046675",
  author = "Antunes, Dinler A and Rigo, Maur\'{i}cio M and Freitas, Martiela V and FA, Mendes Marcus and Sinigaglia, Marialva and Liz\'{e}e, Gregory and Kavraki, Lydia E and Selin, Liisa K and Cornberg, Markus and Vieira, Gustavo F",
  title = "Interpreting {T}-cell cross-reactivity through structure: implications for {TCR}-based cancer immunotherapy",
  journal = "Front. Immunol.",
  volume = "8",
  number = "1210",
  year = "2017",
  keywords = "T-cell cross-reactivity, peptide–MHC complex, cross-reactivity hot-spots, TCR-interacting surface, hierarchical clustering, TCR/pMHC, cancer immunotherapy"
}

Abstract

Immunotherapy has become one of the most promising avenues for cancer treatment, making use of the patient’s own immune system to eliminate cancer cells. Clinical trials with T-cell-based immunotherapies have shown dramatic tumor regressions, being effective in multiple cancer types and for many different patients. Unfortunately, this progress was tempered by reports of serious (even fatal) side effects. Such therapies rely on the use of cytotoxic T-cell lymphocytes, an essential part of the adaptive immune system. Cytotoxic T-cells are regularly involved in surveillance and are capable of both eliminating diseased cells and generating protective immunological memory. The specificity of a given T-cell is determined through the structural interaction between the T-cell receptor (TCR) and a peptide-loaded major histocompatibility complex (MHC); i.e., an intracellular peptide–ligand displayed at the cell surface by an MHC molecule. However, a given TCR can recognize different peptide–MHC (pMHC) complexes, which can sometimes trigger an unwanted response that is referred to as T-cell cross-reactivity. This has become a major safety issue in TCR-based immunotherapies, following reports of melanoma-specific T-cells causing cytotoxic damage to healthy tissues (e.g., heart and nervous system). T-cell cross-reactivity has been extensively studied in the context of viral immunology and tissue transplantation. Growing evidence suggests that it is largely driven by structural similarities of seemingly unrelated pMHC complexes. Here, we review recent reports about the existence of pMHC “hot-spots" for cross-reactivity and propose the existence of a TCR interaction profile (i.e., a refinement of a more general TCR footprint in which some amino acid residues are more important than others in triggering T-cell cross-reactivity). We also make use of available structural data and pMHC models to interpret previously reported cross-reactivity patterns among virus-derived peptides. Our study provides further evidence that structural analyses of pMHC complexes can be used to assess the intrinsic likelihood of cross-reactivity among peptide-targets. Furthermore, we hypothesize that some apparent inconsistencies in reported cross-reactivities, such as a preferential directionality, might also be driven by particular structural features of the targeted pMHC complex. Finally, we explain why TCR-based immunotherapy provides a special context in which meaningful T-cell cross-reactivity predictions can be made.

D. Devaurs, D. A. Antunes, M. Papanastasiou, M. Moll, D. Ricklin, J. D. Lambris, and L. E. Kavraki, “Coarse-grained conformational sampling of protein structure improves the fit to experimental hydrogen-exchange data,” Frontiers in Molecular Biosciences, vol. 4, no. 13, 2017.

pdf publisher

BibTeX

@article{devaurs-17-fmb,
  grants = "NSF AF 1423304,NIH 1R21CA209941",
  abstract = "Monitoring hydrogen/deuterium exchange (HDX) undergone by a protein in solution produces experimental data that translates into valuable information about the protein’s structure. Data produced by HDX experiments is often interpreted using a crystal structure of the protein, when available. However, it has been shown that the correspondence between experimental HDX data and crystal structures is often not satisfactory. This creates difficulties when trying to perform a structural analysis of the HDX data. In this paper, we evaluate several strategies to obtain a conformation providing a good fit to the experimental HDX data, which is a premise of structural analysis. We show that performing molecular dynamics simulations can be inadequate to obtain such conformations, and we propose a novel methodology involving a coarse-grained conformational sampling approach instead. By extensively exploring the intrinsic flexibility of a protein with this approach, we produce a conformational ensemble from which we extract a single conformation providing a good fit to the experimental HDX data. We successfully demonstrate the applicability of our method to four small and medium-sized proteins.",
  keywords = "fundamentals of protein modeling",
  author = "Devaurs, Didier and Antunes, Dinler A. and Papanastasiou, Malvina and Moll, Mark and Ricklin, Daniel and Lambris, John D. and Kavraki, Lydia E.",
  title = "Coarse-grained conformational sampling of protein structure improves the fit to experimental hydrogen-exchange data",
  journal = "Frontiers in Molecular Biosciences",
  volume = "4",
  number = "13",
  doi = "10.3389/fmolb.2017.00013",
  pmcid = "PMC5344923",
  pmid = "28344973",
  year = "2017"
}

Abstract

Monitoring hydrogen/deuterium exchange (HDX) undergone by a protein in solution produces experimental data that translates into valuable information about the protein’s structure. Data produced by HDX experiments is often interpreted using a crystal structure of the protein, when available. However, it has been shown that the correspondence between experimental HDX data and crystal structures is often not satisfactory. This creates difficulties when trying to perform a structural analysis of the HDX data. In this paper, we evaluate several strategies to obtain a conformation providing a good fit to the experimental HDX data, which is a premise of structural analysis. We show that performing molecular dynamics simulations can be inadequate to obtain such conformations, and we propose a novel methodology involving a coarse-grained conformational sampling approach instead. By extensively exploring the intrinsic flexibility of a protein with this approach, we produce a conformational ensemble from which we extract a single conformation providing a good fit to the experimental HDX data. We successfully demonstrate the applicability of our method to four small and medium-sized proteins.

2016

D. Devaurs, M. Papanastasiou, D. A. Antunes, J. R. Abella, M. Moll, D. Ricklin, J. D. Lambris, and L. E. Kavraki, “Native state of complement protein C3d analysed via hydrogen exchange and conformational sampling,” International Journal of Computational Biology and Drug Design, vol. 11, no. 1/2, pp. 90–113, 2016.

pdf publisher

BibTeX

@article{devaurs-16-icibm,
  grants = "NSF AF 1423304,NIH 1R21CA209941",
  author = "Devaurs, Didier and Papanastasiou, Malvina and Antunes, Dinler A. and Abella, Jayvee R. and Moll, Mark and Ricklin, Daniel and Lambris, John D. and Kavraki, Lydia E.",
  title = "Native state of complement protein {C3d} analysed via hydrogen exchange and conformational sampling",
  journal = "International Journal of Computational Biology and Drug Design",
  keywords = "fundamentals of protein modeling",
  volume = "11",
  number = "1/2",
  pages = "90-113",
  doi = "10.1504/IJCBDD.2018.10011903",
  year = "2016",
  abstract = "Hydrogen/deuterium exchange detected by mass spectrometry (HDX-MS) provides valuable information on protein structure and dynamics. Although HDX-MS data is often interpreted using crystal structures, it was suggested that conformational ensembles produced by molecular dynamics simulations yield more accurate interpretations. In this paper, we analyse the complement protein C3d through HDX-MS data and evaluate several interpretation methodologies, using an existing prediction model to derive HDX-MS data from protein structure. We perform an HDX-MS experiment on C3d and, then, to interpret and refine the obtained data, we look for a conformation (or conformational ensemble) of C3d that allows computationally replicating this data. First, we confirm that crystal structures are not a good choice. Second, we suggest that conformational ensembles produced by molecular dynamics simulations might not always be satisfactory either. Finally, we show that coarse-grained conformational sampling of C3d produces a conformation from which the HDX-MS data can be replicated and refined."
}

Abstract

Hydrogen/deuterium exchange detected by mass spectrometry (HDX-MS) provides valuable information on protein structure and dynamics. Although HDX-MS data is often interpreted using crystal structures, it was suggested that conformational ensembles produced by molecular dynamics simulations yield more accurate interpretations. In this paper, we analyse the complement protein C3d through HDX-MS data and evaluate several interpretation methodologies, using an existing prediction model to derive HDX-MS data from protein structure. We perform an HDX-MS experiment on C3d and, then, to interpret and refine the obtained data, we look for a conformation (or conformational ensemble) of C3d that allows computationally replicating this data. First, we confirm that crystal structures are not a good choice. Second, we suggest that conformational ensembles produced by molecular dynamics simulations might not always be satisfactory either. Finally, we show that coarse-grained conformational sampling of C3d produces a conformation from which the HDX-MS data can be replicated and refined.

2015

M. M. Rigo, D. A. Antunes, M. Vaz de Freitas, M. Fabiano de Almeida Mendes, L. Meira, M. Sinigaglia, and G. F. Vieira, “DockTope: a Web-based tool for automated pMHC-I modelling,” Scientific Reports - Nature, vol. 5, p. 18413, Dec. 2015.

publisher

BibTeX

@article{Rigo2015,
  doi = "10.1038/srep18413",
  abstract = "The immune system is constantly challenged, being required to protect the organism against a wide variety of infectious pathogens and, at the same time, to avoid autoimmune disorders. One of the most important molecules involved in these events is the Major Histocompatibility Complex class I (MHC-I), responsible for binding and presenting small peptides from the intracellular environment to CD8+ T cells. The study of peptide:MHC-I (pMHC-I) molecules at a structural level is crucial to understand the molecular mechanisms underlying immunologic responses. Unfortunately, there are few pMHC-I structures in the Protein Data Bank (PDB) (especially considering the total number of complexes that could be formed combining different peptides), and pMHC-I modelling tools are scarce. Here, we present DockTope, a free and reliable web-based tool for pMHC-I modelling, based on crystal structures from the PDB. DockTope is fully automated and allows any researcher to construct a pMHC-I complex in an efficient way. We have reproduced a dataset of 135 non-redundant pMHC-I structures from the PDB (Cα RMSD below 1 Å). Modelling of pMHC-I complexes is remarkably important, contributing to the knowledge of important events such as cross-reactivity, autoimmunity, cancer therapy, transplantation and rational vaccine design.",
  author = "Rigo, M. M. and Antunes, D. A. and Vaz de Freitas, M. and Fabiano de Almeida Mendes, M. and Meira, L. and Sinigaglia, M. and Vieira, G. F.",
  title = "{D}ock{T}ope: a {W}eb-based tool for automated p{M}{H}{C}-{I} modelling",
  journal = "Scientific Reports - Nature",
  year = "2015",
  volume = "5",
  pages = "18413",
  month = dec
}

Abstract

The immune system is constantly challenged, being required to protect the organism against a wide variety of infectious pathogens and, at the same time, to avoid autoimmune disorders. One of the most important molecules involved in these events is the Major Histocompatibility Complex class I (MHC-I), responsible for binding and presenting small peptides from the intracellular environment to CD8+ T cells. The study of peptide:MHC-I (pMHC-I) molecules at a structural level is crucial to understand the molecular mechanisms underlying immunologic responses. Unfortunately, there are few pMHC-I structures in the Protein Data Bank (PDB) (especially considering the total number of complexes that could be formed combining different peptides), and pMHC-I modelling tools are scarce. Here, we present DockTope, a free and reliable web-based tool for pMHC-I modelling, based on crystal structures from the PDB. DockTope is fully automated and allows any researcher to construct a pMHC-I complex in an efficient way. We have reproduced a dataset of 135 non-redundant pMHC-I structures from the PDB (Cα RMSD below 1 Å). Modelling of pMHC-I complexes is remarkably important, contributing to the knowledge of important events such as cross-reactivity, autoimmunity, cancer therapy, transplantation and rational vaccine design.

M. F. Mendes, D. A. Antunes, M. M. Rigo, M. Sinigaglia, and G. F. Vieira, “Improved structural method for T-cell cross-reactivity prediction,” Molecular Immunology, vol. 67, no. 2 Pt B, pp. 303–310, Oct. 2015.

publisher

BibTeX

@article{Mendes2015,
  doi = "10.1016/j.molimm.2015.06.017",
  abstract = "Cytotoxic T-lymphocytes (CTLs) are the key players of adaptive cellular immunity, being able to identify and eliminate infected cells through the interaction with peptide-loaded major histocompatibility complexes class I (pMHC-I). Despite the high specificity of this interaction, a given lymphocyte is actually able to recognize more than just one pMHC-I complex, a phenomenon referred as cross-reactivity. In the present work we describe the use of pMHC-I structural features as input for multivariate statistical methods, to perform standardized structure-based predictions of cross-reactivity among viral epitopes. Our improved approach was able to successfully identify cross-reactive targets among 28 naturally occurring hepatitis C virus (HCV) variants and among eight epitopes from the four dengue virus serotypes. In both cases, our results were supported by multiscale bootstrap resampling and by data from previously published in vitro experiments. The combined use of data from charges and accessible surface area (ASA) of selected residues over the pMHC-I surface provided a powerful way of assessing the structural features involved in triggering cross-reactive responses. Moreover, the use of an R package (pvclust) for assessing the uncertainty in the hierarchical cluster analysis provided a statistical support for the interpretation of results. Taken together, these methods can be applied to vaccine design, both for the selection of candidates capable of inducing immunity against different targets, or to identify epitopes that could trigger undesired immunological responses.",
  author = "Mendes, M. F. and Antunes, D. A. and Rigo, M. M. and Sinigaglia, M. and Vieira, G. F.",
  title = "Improved structural method for {T}-cell cross-reactivity prediction",
  journal = "Molecular Immunology",
  year = "2015",
  volume = "67",
  number = "2 Pt B",
  pages = "303--310",
  month = oct
}

Abstract

Cytotoxic T-lymphocytes (CTLs) are the key players of adaptive cellular immunity, being able to identify and eliminate infected cells through the interaction with peptide-loaded major histocompatibility complexes class I (pMHC-I). Despite the high specificity of this interaction, a given lymphocyte is actually able to recognize more than just one pMHC-I complex, a phenomenon referred as cross-reactivity. In the present work we describe the use of pMHC-I structural features as input for multivariate statistical methods, to perform standardized structure-based predictions of cross-reactivity among viral epitopes. Our improved approach was able to successfully identify cross-reactive targets among 28 naturally occurring hepatitis C virus (HCV) variants and among eight epitopes from the four dengue virus serotypes. In both cases, our results were supported by multiscale bootstrap resampling and by data from previously published in vitro experiments. The combined use of data from charges and accessible surface area (ASA) of selected residues over the pMHC-I surface provided a powerful way of assessing the structural features involved in triggering cross-reactive responses. Moreover, the use of an R package (pvclust) for assessing the uncertainty in the hierarchical cluster analysis provided a statistical support for the interpretation of results. Taken together, these methods can be applied to vaccine design, both for the selection of candidates capable of inducing immunity against different targets, or to identify epitopes that could trigger undesired immunological responses.

S. Zhang, R. K. Bakshi, P. V. Suneetha, P. Fytili, D. A. Antunes, G. F. Vieira, R. Jacobs, C. S. Klade, M. P. Manns, A. R. Kraft, H. Wedemeyer, V. Schlaphoff, and M. Cornberg, “Frequency, private specificity, and cross-reactivity of preexisting hepatitis C virus (HCV)-specific CD8+ T cells in HCV-seronegative individuals: implications for vaccine responses,” Journal of Virology, vol. 89, no. 16, pp. 8304–8317, Aug. 2015.

publisher

BibTeX

@article{Zhang2015,
  doi = "10.1128/JVI.00539-15",
  abstract = "T cell responses play a critical role in controlling or clearing viruses. Therefore, strategies to prevent or treat infections include boosting T cell responses. T cells specific for various pathogens have been reported in unexposed individuals and an influence of such cells on the response toward vaccines is conceivable. However, little is known about their frequency, repertoire, and impact on vaccination. We performed a detailed characterization of CD8+ T cells specific to a hepatitis C virus (HCV) epitope (NS3-1073) in 121 HCV-seronegative individuals. We show that in vitro HCV NS3-1073-specific CD8+ T cell responses were rather abundantly detectable in one-third of HCV-seronegative individuals irrespective of risk factors for HCV exposure. Ex vivo, these NS3-1073-specific CD8+ T cells were found to be both naive and memory cells. Importantly, recognition of various peptides derived from unrelated viruses by NS3-1073-specific CD8+ T cells showed a considerable degree of T cell cross-reactivity, suggesting that they might in part originate from previous heterologous infections. Finally, we further provide evidence that preexisting NS3-1073-specific CD8+ T cells can impact the T cell response toward peptide vaccination. Healthy, vaccinated individuals who showed an in vitro response toward NS3-1073 already before vaccination displayed a more vigorous and earlier response toward the vaccine.",
  author = "Zhang, S. and Bakshi, R. K. and Suneetha, P. V. and Fytili, P. and Antunes, D. A. and Vieira, G. F. and Jacobs, R. and Klade, C. S. and Manns, M. P. and Kraft, A. R. and Wedemeyer, H. and Schlaphoff, V. and Cornberg, M.",
  title = "{F}requency, private specificity, and cross-reactivity of preexisting hepatitis {C} virus ({H}{C}{V})-specific {C}{D}8+ {T} cells in {H}{C}{V}-seronegative individuals: implications for vaccine responses",
  journal = "Journal of Virology",
  year = "2015",
  volume = "89",
  number = "16",
  pages = "8304--8317",
  month = aug
}

Abstract

T cell responses play a critical role in controlling or clearing viruses. Therefore, strategies to prevent or treat infections include boosting T cell responses. T cells specific for various pathogens have been reported in unexposed individuals and an influence of such cells on the response toward vaccines is conceivable. However, little is known about their frequency, repertoire, and impact on vaccination. We performed a detailed characterization of CD8+ T cells specific to a hepatitis C virus (HCV) epitope (NS3-1073) in 121 HCV-seronegative individuals. We show that in vitro HCV NS3-1073-specific CD8+ T cell responses were rather abundantly detectable in one-third of HCV-seronegative individuals irrespective of risk factors for HCV exposure. Ex vivo, these NS3-1073-specific CD8+ T cells were found to be both naive and memory cells. Importantly, recognition of various peptides derived from unrelated viruses by NS3-1073-specific CD8+ T cells showed a considerable degree of T cell cross-reactivity, suggesting that they might in part originate from previous heterologous infections. Finally, we further provide evidence that preexisting NS3-1073-specific CD8+ T cells can impact the T cell response toward peptide vaccination. Healthy, vaccinated individuals who showed an in vitro response toward NS3-1073 already before vaccination displayed a more vigorous and earlier response toward the vaccine.

D. A. Antunes, D. Devaurs, and L. E. Kavraki, “Understanding the challenges of protein flexibility in drug design,” Expert Opinion on Drug Discovery, vol. 10, no. 12, pp. 1301–1313, 2015.

pdf publisher

BibTeX

@article{antunes-15-eodd,
  grants = "NSF AF 1423304,NSF ABI 1262491",
  author = "Antunes, Dinler A. and Devaurs, Didier and Kavraki, Lydia E.",
  title = "Understanding the challenges of protein flexibility in drug design",
  journal = "Expert Opinion on Drug Discovery",
  year = "2015",
  volume = "10",
  number = "12",
  pages = "1301--1313",
  keywords = "proteins and drugs",
  doi = "10.1517/17460441.2015.1094458",
  pmid = "26414598",
  abstract = "Introduction: Protein–ligand interactions play key roles in various metabolic pathways, and the proteins involved in these interactions represent major targets for drug discovery. Molecular docking is widely used to predict the structure of protein–ligand complexes, and protein flexibility stands out as one of the most important and challenging issues for binding mode prediction. Various docking methods accounting for protein flexibility have been proposed, tackling problems of ever-increasing dimensionality.
  
  Areas covered: This paper presents an overview of conformational sampling methods treating target flexibility during molecular docking. Special attention is given to approaches considering full protein flexibility. Contrary to what is frequently done, this review does not rely on classical biomolecular recognition models to classify existing docking methods. Instead, it applies algorithmic considerations, focusing on the level of flexibility accounted for. This review also discusses the diversity of docking applications, from virtual screening (VS) of small drug-like compounds to geometry prediction (GP) of protein–peptide complexes.
  
  Expert opinion: Considering the diversity of docking methods presented here, deciding which one is the best at treating protein flexibility depends on the system under study and the research application. In VS experiments, ensemble docking can be used to implicitly account for large-scale conformational changes, and selective docking can additionally consider local binding-site rearrangements. In other cases, on-the-fly exploration of the whole protein–ligand complex might be needed for accurate GP of the binding mode. Among other things, future methods are expected to provide alternative binding modes, which will better reflect the dynamic nature of protein–ligand interactions."
}

Abstract

Introduction: Protein–ligand interactions play key roles in various metabolic pathways, and the proteins involved in these interactions represent major targets for drug discovery. Molecular docking is widely used to predict the structure of protein–ligand complexes, and protein flexibility stands out as one of the most important and challenging issues for binding mode prediction. Various docking methods accounting for protein flexibility have been proposed, tackling problems of ever-increasing dimensionality. Areas covered: This paper presents an overview of conformational sampling methods treating target flexibility during molecular docking. Special attention is given to approaches considering full protein flexibility. Contrary to what is frequently done, this review does not rely on classical biomolecular recognition models to classify existing docking methods. Instead, it applies algorithmic considerations, focusing on the level of flexibility accounted for. This review also discusses the diversity of docking applications, from virtual screening (VS) of small drug-like compounds to geometry prediction (GP) of protein–peptide complexes. Expert opinion: Considering the diversity of docking methods presented here, deciding which one is the best at treating protein flexibility depends on the system under study and the research application. In VS experiments, ensemble docking can be used to implicitly account for large-scale conformational changes, and selective docking can additionally consider local binding-site rearrangements. In other cases, on-the-fly exploration of the whole protein–ligand complex might be needed for accurate GP of the binding mode. Among other things, future methods are expected to provide alternative binding modes, which will better reflect the dynamic nature of protein–ligand interactions.

2014

D. F. Figueiredo, D. A. Antunes, M. M. Rigo, M. F. de Almeida Mendes, J. P. Silva, F. Q. Mayer, U. Matte, R. Giugliani, G. F. Vieira, and M. Sinigaglia, “Lessons from molecular modeling human α-L-iduronidase,” Journal of Molecular Graphics and Modelling, vol. 54, pp. 107–113, Nov. 2014.

publisher

BibTeX

@article{Figueiredo2014-idua,
  pmid = "25459762",
  doi = "10.1016/j.jmgm.2014.10.004",
  author = "Figueiredo, Danieli Forgiarini and Antunes, Dinler Amaral and Rigo, Maur\'{i}cio Menegatti and de Almeida Mendes, Marcus Fabiano and Silva, Jader Peres and Mayer, Fabiana Qoos and Matte, Ursula and Giugliani, Roberto and Vieira, Gustavo Fioravanti and Sinigaglia, Marialva",
  title = "Lessons from molecular modeling human $\alpha$-L-iduronidase",
  journal = "Journal of Molecular Graphics and Modelling",
  year = "2014",
  volume = "54",
  pages = "107--113",
  month = nov,
  abstract = "Human $\alpha$-L-iduronidase (IDUA) is a member of glycoside hydrolase family and is involved in the catabolism of glycosaminoglycans (GAGs), heparan sulfate (HS) and dermatan sulfate (DS). Mutations in this enzyme are responsible for mucopolysaccharidosis I (MPS I), an inherited lysosomal storage disorder. Despite great interest in determining and studying this enzyme structure, the lack of a high identity to templates and other technical issues have challenged both bioinformaticians and crystallographers, until the recent publication of an IDUA crystal structure (PDB: 4JXP). In the present work, four alternative IDUA models, generated and evaluated prior to crystallographic determination, were compared to the 4JXP structure. A combined analysis using several viability assessment tools and molecular dynamics simulations highlights the strengths and limitations of different comparative modeling protocols, all of which are based on the same low identity template (only 22\%). Incorrect alignment between the target and template was confirmed to be a major bottleneck in homology modeling, regardless of the modeling software used. Moreover, secondary structure analysis during a 50ns simulation seems to be useful for indicating alignment errors and structural instabilities. The best model was achieved through the combined use of Phyre 2 and Modeller, suggesting the use of this protocol for the modeling of other proteins that still lack high identity templates."
}

Abstract

Human α-L-iduronidase (IDUA) is a member of glycoside hydrolase family and is involved in the catabolism of glycosaminoglycans (GAGs), heparan sulfate (HS) and dermatan sulfate (DS). Mutations in this enzyme are responsible for mucopolysaccharidosis I (MPS I), an inherited lysosomal storage disorder. Despite great interest in determining and studying this enzyme structure, the lack of a high identity to templates and other technical issues have challenged both bioinformaticians and crystallographers, until the recent publication of an IDUA crystal structure (PDB: 4JXP). In the present work, four alternative IDUA models, generated and evaluated prior to crystallographic determination, were compared to the 4JXP structure. A combined analysis using several viability assessment tools and molecular dynamics simulations highlights the strengths and limitations of different comparative modeling protocols, all of which are based on the same low identity template (only 22%). Incorrect alignment between the target and template was confirmed to be a major bottleneck in homology modeling, regardless of the modeling software used. Moreover, secondary structure analysis during a 50ns simulation seems to be useful for indicating alignment errors and structural instabilities. The best model was achieved through the combined use of Phyre 2 and Modeller, suggesting the use of this protocol for the modeling of other proteins that still lack high identity templates.

D. A. Antunes, “Peptide-MHC structural similarity as a probability for cross-reactive T cell responses,” PhD thesis, Federal University of Rio Grande do Sul, Graduate Program in Genetics and Molecular Biology (PPGBM), 2014. Language: Portuguese

pdf publisher

BibTeX

@phdthesis{Antunes-DS,
  author = "Antunes, D. A.",
  title = "Peptide-MHC structural similarity as a probability for cross-reactive T cell responses",
  school = "Federal University of Rio Grande do Sul, Graduate Program in Genetics and Molecular Biology (PPGBM)",
  year = "2014",
  month = aug,
  note = "Language: Portuguese",
  url = "https://www.lume.ufrgs.br/handle/10183/104800",
  abstract = "Host-pathogen coevolution can be implicated as one of the main features driving the great diversity of genes involved with immunological response. The so-called “MHC region” (Major Histocompatibility Complex), located at the short arm of human chromosome 6, is the most polymorphic and dense region of our genome. The three most polymorphic genes in this locus encode the heavy chain of a complex referred as MHC class I, which is responsible for presentation (at cell surface) of peptides derived from the digestion of cytosolic proteins. This mechanism plays a key role in antiviral immune response, allowing infected cells to be identified and eliminated by Cytotoxic T Lymphocytes. Although structurally similar, each MHC molecule presents higher affinity for peptides with certain biochemical properties. Therefore, the greater the variability of MHCs in a given population, the smaller the risk that all individuals are unable to present at least some targets derived from a given virus. On the other hand, cellular immune response and memory generation against the target presented by the MHC, depends on specific recognition of this peptide:MHC (pMHC) complex by a given T cell population. In this work, we use bioinformatics tools to perform structural analysis of pMHC complexes, identifying features involved in triggering cellular immune responses. Our in silico results, corroborated by in vitro and ex vivo experiments suggest that structural similarity among pMHC complexes (topography and electrostatic potential) plays a central role in cross-reactivity of cytotoxic T cells, with implications over heterologous immunity, immunopathology and vaccine development."
}

Abstract

Host-pathogen coevolution can be implicated as one of the main features driving the great diversity of genes involved with immunological response. The so-called “MHC region” (Major Histocompatibility Complex), located at the short arm of human chromosome 6, is the most polymorphic and dense region of our genome. The three most polymorphic genes in this locus encode the heavy chain of a complex referred as MHC class I, which is responsible for presentation (at cell surface) of peptides derived from the digestion of cytosolic proteins. This mechanism plays a key role in antiviral immune response, allowing infected cells to be identified and eliminated by Cytotoxic T Lymphocytes. Although structurally similar, each MHC molecule presents higher affinity for peptides with certain biochemical properties. Therefore, the greater the variability of MHCs in a given population, the smaller the risk that all individuals are unable to present at least some targets derived from a given virus. On the other hand, cellular immune response and memory generation against the target presented by the MHC, depends on specific recognition of this peptide:MHC (pMHC) complex by a given T cell population. In this work, we use bioinformatics tools to perform structural analysis of pMHC complexes, identifying features involved in triggering cellular immune responses. Our in silico results, corroborated by in vitro and ex vivo experiments suggest that structural similarity among pMHC complexes (topography and electrostatic potential) plays a central role in cross-reactivity of cytotoxic T cells, with implications over heterologous immunity, immunopathology and vaccine development.

D. A. Antunes, M. M. Rigo, M. Sinigaglia, R. M. de Medeiros, D. M. Junqueira, S. E. Almeida, and G. F. Vieira, “New insights into the in silico prediction of HIV protease resistance to nelfinavir,” PLoS ONE, vol. 9, no. 1, p. e87520, 2014.

pdf publisher

BibTeX

@article{antunes2014-hiv,
  pmid = "24498124",
  doi = "10.1371/journal.pone.0087520",
  author = "Antunes, D. A. and Rigo, M. M. and Sinigaglia, M. and de Medeiros, R. M. and Junqueira, D. M. and Almeida, S. E. and Vieira, G. F.",
  title = "{N}ew insights into the in silico prediction of {H}{I}{V} protease resistance to nelfinavir",
  journal = "PLoS ONE",
  year = "2014",
  volume = "9",
  number = "1",
  pages = "e87520",
  abstract = "The Human Immunodeficiency Virus type 1 protease enzyme (HIV-1 PR) is one of the most important targets of antiretroviral therapy used in the treatment of AIDS patients. The success of protease-inhibitors (PIs), however, is often limited by the emergence of protease mutations that can confer resistance to a specific drug, or even to multiple PIs. In the present study, we used bioinformatics tools to evaluate the impact of the unusual mutations D30V and V32E over the dynamics of the PR-Nelfinavir complex, considering that codons involved in these mutations were previously related to major drug resistance to Nelfinavir. Both studied mutations presented structural features that indicate resistance to Nelfinavir, each one with a different impact over the interaction with the drug. The D30V mutation triggered a subtle change in the PR structure, which was also observed for the well-known Nelfinavir resistance mutation D30N, while the V32E exchange presented a much more dramatic impact over the PR flap dynamics. Moreover, our in silico approach was also able to describe different binding modes of the drug when bound to different proteases, identifying specific features of HIV-1 subtype B and subtype C proteases."
}

Abstract

The Human Immunodeficiency Virus type 1 protease enzyme (HIV-1 PR) is one of the most important targets of antiretroviral therapy used in the treatment of AIDS patients. The success of protease-inhibitors (PIs), however, is often limited by the emergence of protease mutations that can confer resistance to a specific drug, or even to multiple PIs. In the present study, we used bioinformatics tools to evaluate the impact of the unusual mutations D30V and V32E over the dynamics of the PR-Nelfinavir complex, considering that codons involved in these mutations were previously related to major drug resistance to Nelfinavir. Both studied mutations presented structural features that indicate resistance to Nelfinavir, each one with a different impact over the interaction with the drug. The D30V mutation triggered a subtle change in the PR structure, which was also observed for the well-known Nelfinavir resistance mutation D30N, while the V32E exchange presented a much more dramatic impact over the PR flap dynamics. Moreover, our in silico approach was also able to describe different binding modes of the drug when bound to different proteases, identifying specific features of HIV-1 subtype B and subtype C proteases.

2013

M. Sinigaglia, D. A. Antunes, M. M. Rigo, J. A. Chies, and G. F. Vieira, “CrossTope: a curate repository of 3D structures of immunogenic peptide: MHC complexes,” Database (Oxford), vol. 2013, p. bat002, 2013.

pdf publisher

BibTeX

@article{sinigaglia2013-crosstope,
  doi = "10.1093/database/bat002",
  abstract = "The CrossTope is a highly curate repository of three-dimensional structures of peptide:major histocompatibility complex (MHC) class I complexes (pMHC-I). The complexes hosted by this databank were obtained in protein databases and by large-scale in silico construction of pMHC-I structures, using a new approach developed by our group. At this moment, the database contains 182 'non-redundant' pMHC-I complexes from two human and two murine alleles. A web server provides interface for database query. The user can download (i) structure coordinate files and (ii) topological and charges distribution maps images from the T-cell receptor-interacting surface of pMHC-I complexes. The retrieved structures and maps can be used to cluster similar epitopes in cross-reactivity approaches, to analyse viral escape mutations in a structural level or even to improve the immunogenicity of tumour antigens. Database URL: http://www.crosstope.com.br.",
  author = "Sinigaglia, M. and Antunes, D. A. and Rigo, M. M. and Chies, J. A. and Vieira, G. F.",
  title = "{C}ross{T}ope: a curate repository of 3{D} structures of immunogenic peptide: {M}{H}{C} complexes",
  journal = "Database (Oxford)",
  year = "2013",
  volume = "2013",
  pages = "bat002"
}

Abstract

The CrossTope is a highly curate repository of three-dimensional structures of peptide:major histocompatibility complex (MHC) class I complexes (pMHC-I). The complexes hosted by this databank were obtained in protein databases and by large-scale in silico construction of pMHC-I structures, using a new approach developed by our group. At this moment, the database contains 182 ’non-redundant’ pMHC-I complexes from two human and two murine alleles. A web server provides interface for database query. The user can download (i) structure coordinate files and (ii) topological and charges distribution maps images from the T-cell receptor-interacting surface of pMHC-I complexes. The retrieved structures and maps can be used to cluster similar epitopes in cross-reactivity approaches, to analyse viral escape mutations in a structural level or even to improve the immunogenicity of tumour antigens. Database URL: http://www.crosstope.com.br.

2012

M. M. Rigo, D. A. Antunes, S. P. Cibulski, M. Sinigaglia, J. A. Chies, and G. F. Vieira, “Immunogenic epitopes of Hantaviruses’ N protein are restricted to conserved regions,” Frontiers in Bioscience (Landmark Ed), vol. 17, pp. 1582–1588, Jan. 2012.

publisher

BibTeX

@article{Rigo2012-hanta,
  pmid = "22201821",
  doi = "10.2741/4004",
  author = "Rigo, M. M. and Antunes, D. A. and Cibulski, S. P. and Sinigaglia, M. and Chies, J. A. and Vieira, G. F.",
  title = "{I}mmunogenic epitopes of {H}antaviruses' {N} protein are restricted to conserved regions",
  journal = "Frontiers in Bioscience (Landmark Ed)",
  year = "2012",
  volume = "17",
  pages = "1582--1588",
  month = jan,
  abstract = "The Bunyaviridae virus family is composed by five genera, of which the Hantavirus genus is one of the most important representatives. Occasionally, these viruses can be transmitted to humans, giving rise to severe diseases that present high mortality rates. We analyzed the amino acid sequences of the nucleocapsid (N) proteins of 34 different hantaviruses to investigate the potential mechanisms involved in immunogenicity against hantaviruses. Immunogenic epitopes described in the literature through experimental analyses for Sin Nombre (SNV), Puumala (PUUV), and Hantaan (HTNV) viruses' species were retrieved. We identified and characterized the regions believed to be responsible for the induction of immune response in hosts. We found that N protein epitopes described in the literature for PUUV, SNV and HTNV viruses are all located in highly conserved regions of the protein. The high conservation of these regions suggests that a cross-reactive immune response among different hantaviruses can be induced."
}

Abstract

The Bunyaviridae virus family is composed by five genera, of which the Hantavirus genus is one of the most important representatives. Occasionally, these viruses can be transmitted to humans, giving rise to severe diseases that present high mortality rates. We analyzed the amino acid sequences of the nucleocapsid (N) proteins of 34 different hantaviruses to investigate the potential mechanisms involved in immunogenicity against hantaviruses. Immunogenic epitopes described in the literature through experimental analyses for Sin Nombre (SNV), Puumala (PUUV), and Hantaan (HTNV) viruses’ species were retrieved. We identified and characterized the regions believed to be responsible for the induction of immune response in hosts. We found that N protein epitopes described in the literature for PUUV, SNV and HTNV viruses are all located in highly conserved regions of the protein. The high conservation of these regions suggests that a cross-reactive immune response among different hantaviruses can be induced.

M. M. Rigo, D. A. Antunes, M. Sinigaglia, J. A. B. Chies, and G. F. Vieira, “MHC, Viral Infection and Immunoinformatics,” in Major Histocompatibility Complex: Biology, Functions and Roles in Disease, Nova Science Publishers, 2012, pp. 69–85.

publisher

BibTeX

@inbook{Rigo-NP12,
  author = "Rigo, M. M. and Antunes, D. A. and Sinigaglia, M. and Chies, J. A. B. and Vieira, GF.",
  title = "MHC, Viral Infection and Immunoinformatics",
  booktitle = "Major Histocompatibility Complex: Biology, Functions and Roles in Disease",
  series = "Immunology and Immune System Disorders",
  pages = "69--85",
  publisher = "Nova Science Publishers",
  year = "2012",
  isbn = "978-1-61942-999-4",
  url = "https://novapublishers.com/shop/major-histocompatibility-complex-biology-functions-and-roles-in-disease/",
  abstract = "The immunologic surveillance is performed by several molecules (e.g. antibodies and complement system) and cells (e.g. T lymphocytes, Natural Killer cells) which are triggered depending on the type of infection. In the specific context of the viral infection, a special set of molecules, named Major Complex of Histocompatibility class I (MHC-I), which is represented by the Human Leukocyte Antigen (HLA) genes in humans, perform an essential role. These molecules are responsible for the presentation of small peptides from viruses, parasites, bacteria or even from human proteins to the T Cell Lymphocytes, which will or will not trigger an immune response, through the T Cell Receptor (TCR) interaction. There are two classes of MHC: MHC-I and MHC-II. The MHC-I is responsible for the presentation of small peptides (8-12 amino acids long) generated by endogenous processing, while the MHC-II presents longer peptides (up to 30 amino acids) from exogenous environment. Regarding MHC-I presentation it is known that topologies and electrostatics patterns of the peptide:MHC (pMHC) interacting region are important for TCR recognition. Also, the affinity and stability between the peptide (epitope) and the MHC (receptor) play a crucial role, especially on what concerns electrostatics and non-hydrophobic interactions between the MHC and anchor residues of the peptide. All these features stand for the peptide immunogenicity. The knowledgment of issues regarding peptide affinity by MHC cleft and the stability of resulting complex is pivotal for vaccine development field. Nowadays, there are several immunoinformatics tools available to assist the resolution of this task. For instance, peptide binding affinity could be predicted through several computational methods (i.e. using tools that consider amino acid sequence composition and preferential anchor residues occurrence). There are also bioinformatics tools that are not specific for immunological issues, but can be used for this purpose, such as molecular docking and molecular dynamics. The latter can be used to assess pMHC complex stability, one of the main factors involved in peptide presentation. In the present chapter we intend to review basic molecular aspects regarding the MHC-I molecule and its interaction with different epitopes. Also, the use of immunoinformatics aiming to understand the different levels of MHC-I interaction will be discussed."
}

Abstract

The immunologic surveillance is performed by several molecules (e.g. antibodies and complement system) and cells (e.g. T lymphocytes, Natural Killer cells) which are triggered depending on the type of infection. In the specific context of the viral infection, a special set of molecules, named Major Complex of Histocompatibility class I (MHC-I), which is represented by the Human Leukocyte Antigen (HLA) genes in humans, perform an essential role. These molecules are responsible for the presentation of small peptides from viruses, parasites, bacteria or even from human proteins to the T Cell Lymphocytes, which will or will not trigger an immune response, through the T Cell Receptor (TCR) interaction. There are two classes of MHC: MHC-I and MHC-II. The MHC-I is responsible for the presentation of small peptides (8-12 amino acids long) generated by endogenous processing, while the MHC-II presents longer peptides (up to 30 amino acids) from exogenous environment. Regarding MHC-I presentation it is known that topologies and electrostatics patterns of the peptide:MHC (pMHC) interacting region are important for TCR recognition. Also, the affinity and stability between the peptide (epitope) and the MHC (receptor) play a crucial role, especially on what concerns electrostatics and non-hydrophobic interactions between the MHC and anchor residues of the peptide. All these features stand for the peptide immunogenicity. The knowledgment of issues regarding peptide affinity by MHC cleft and the stability of resulting complex is pivotal for vaccine development field. Nowadays, there are several immunoinformatics tools available to assist the resolution of this task. For instance, peptide binding affinity could be predicted through several computational methods (i.e. using tools that consider amino acid sequence composition and preferential anchor residues occurrence). There are also bioinformatics tools that are not specific for immunological issues, but can be used for this purpose, such as molecular docking and molecular dynamics. The latter can be used to assess pMHC complex stability, one of the main factors involved in peptide presentation. In the present chapter we intend to review basic molecular aspects regarding the MHC-I molecule and its interaction with different epitopes. Also, the use of immunoinformatics aiming to understand the different levels of MHC-I interaction will be discussed.

D. A. Antunes, M. M. Rigo, M. Sinigaglia, and G. F. Vieira, “Structural Immunoinformatics and Vaccine Development,” in Bioinformatics Research: New Developments, Nova Science Publishers, 2012, pp. 1–33.

publisher

BibTeX

@inbook{Antunes-NP12,
  author = "Antunes, D. A. and Rigo, M. M. and Sinigaglia, M. and Vieira, G. F.",
  title = "Structural Immunoinformatics and Vaccine Development",
  booktitle = "Bioinformatics Research: New Developments",
  series = " Biotechnology in Agriculture, Industry and Medicine",
  pages = "1--33",
  publisher = "Nova Science Publishers",
  year = "2012",
  isbn = "978-1-61942-363-3",
  url = "http://www.novapublishers.org/catalog/product_info.php?products_id=27160",
  abstract = "Bioinformatics is a new field of research, with roots in computer science, physics, statistics and molecular biology. Its growth was fueled by the genome sequencing initiatives and the great amount of data produced by them, having quickly spread to all other fields of biological research. This innovative way of making science proved to be especially useful to immunology, allowing the development of a range of tools to predict critical steps in the immune response. The Immunological bioinformatics, or just Immunoinformatics, has emerged as a promising field, with possible applications in cancer research and vaccine development. Most of these immunoinformatics tools and databases are based on sequence analysis, which brings advantages, but also some limitations. Bioinformatics, however, has already developed ways to deal with tridimensional structures, allowing studying protein-protein interaction at an atomic level and extract, from each amino acid in these proteins, much more information than that provided by the linear sequence. In this chapter, we will discuss new uses of structural bioinformatics to study immunological problems, focusing on the Major Histocompatibility Complex (MHC). The MHC molecule plays a key role in the cellular immune response, presenting peptides derived from intracellular proteins to Cytotoxic T Lymphocytes (CTLs). Through the interaction between these peptide:MHC complexes (pMHCs) and the T Cell Receptors (TCRs), these CTLs are capable of identifying non-self peptides and eliminate infected or transformed cells. Structural bioinformatics tools, such as Molecular Docking and Molecular Dynamics, are being used to build the tridimensional structure of unknown pMHC complexes – not yet determined by classical experimental methods – and to assess molecular aspects of these structures. For instance, the electrostatic potential of the TCR-interacting surface of these pMHCs, as well as Accessible Surface Area (ASA) of the peptide inside the MHC cleft, can be related to the immunogenicity of a given virus-derived epitope. Moreover, these features can be included in one global analysis that can cover most of many details underlying TCR/pMHC interaction, affording to predict the impact of viral escape mutations and selection of epitopes with potential to induce cross-reactive immune responses."
}

Abstract

Bioinformatics is a new field of research, with roots in computer science, physics, statistics and molecular biology. Its growth was fueled by the genome sequencing initiatives and the great amount of data produced by them, having quickly spread to all other fields of biological research. This innovative way of making science proved to be especially useful to immunology, allowing the development of a range of tools to predict critical steps in the immune response. The Immunological bioinformatics, or just Immunoinformatics, has emerged as a promising field, with possible applications in cancer research and vaccine development. Most of these immunoinformatics tools and databases are based on sequence analysis, which brings advantages, but also some limitations. Bioinformatics, however, has already developed ways to deal with tridimensional structures, allowing studying protein-protein interaction at an atomic level and extract, from each amino acid in these proteins, much more information than that provided by the linear sequence. In this chapter, we will discuss new uses of structural bioinformatics to study immunological problems, focusing on the Major Histocompatibility Complex (MHC). The MHC molecule plays a key role in the cellular immune response, presenting peptides derived from intracellular proteins to Cytotoxic T Lymphocytes (CTLs). Through the interaction between these peptide:MHC complexes (pMHCs) and the T Cell Receptors (TCRs), these CTLs are capable of identifying non-self peptides and eliminate infected or transformed cells. Structural bioinformatics tools, such as Molecular Docking and Molecular Dynamics, are being used to build the tridimensional structure of unknown pMHC complexes – not yet determined by classical experimental methods – and to assess molecular aspects of these structures. For instance, the electrostatic potential of the TCR-interacting surface of these pMHCs, as well as Accessible Surface Area (ASA) of the peptide inside the MHC cleft, can be related to the immunogenicity of a given virus-derived epitope. Moreover, these features can be included in one global analysis that can cover most of many details underlying TCR/pMHC interaction, affording to predict the impact of viral escape mutations and selection of epitopes with potential to induce cross-reactive immune responses.

D. A. Antunes, D. F. Figueiredo, M. M. Rigo, J. P. Silva, J. A. Chies, M. Sinigaglia, and G. F. Vieira, “Hierarchical Clustering of pMHC Complexes Based on the Electrostatic Potential of the TCR-Interacting Surface,” in Second International Society for Computational Biology Latin American regional meeting (ISCB-Latin America), 2012. Santiago, Chile (Best poster award)

BibTeX

@inproceedings{Antunes2012-ISCB,
  author = "Antunes, D. A. and Figueiredo, D. F. and Rigo, M. M. and Silva, J. P. and Chies, J. A. and Sinigaglia, M. and Vieira, G. F.",
  title = "Hierarchical Clustering of pMHC Complexes Based on the Electrostatic Potential of the TCR-Interacting Surface",
  booktitle = "Second International Society for Computational Biology Latin American regional meeting (ISCB-Latin America)",
  year = "2012",
  note = "Santiago, Chile (Best poster award)"
}

M. M. Rigo, D. A. Antunes, R. Minozzo, J. P. Silva, D. F. Figueiredo, J. A. Chies, M. Sinigaglia, and G. F. Vieira, “Analysis of Interaction Residues Between HLA-A*02:01 Cleft and Epitopes,” in Second International Society for Computational Biology Latin American regional meeting (ISCB-Latin America), 2012. Santiago, Chile (Short-Article)

BibTeX

@inproceedings{Rigo2012-ISCB,
  author = "Rigo, M. M. and Antunes, D. A. and Minozzo, R. and Silva, J. P. and Figueiredo, D. F. and Chies, J. A. and Sinigaglia, M. and Vieira, G. F.",
  title = "Analysis of Interaction Residues Between HLA-A*02:01 Cleft and Epitopes",
  booktitle = "Second International Society for Computational Biology Latin American regional meeting (ISCB-Latin America)",
  year = "2012",
  note = "Santiago, Chile (Short-Article)"
}

2011

D. A. Antunes, M. M. Rigo, J. P. Silva, S. P. Cibulski, M. Sinigaglia, J. A. Chies, and G. F. Vieira, “Structural in silico analysis of cross-genotype-reactivity among naturally occurring HCV NS3-1073-variants in the context of HLA-A*02:01 allele,” Molecular Immunology, vol. 48, no. 12-13, pp. 1461–1467, July 2011.

publisher

BibTeX

@article{Antunes2011,
  doi = "10.1016/j.molimm.2011.03.019",
  abstract = "Cellular immune response plays a central role in outcome of Hepatitis C Virus (HCV) infection. While specific T-cell responses are related to viral clearance, impaired responses can lead to chronic infection, turning HCV variability into a major obstacle for vaccine development. In a recent work, Fytili et al. (2008) studied the cross reactive potential of HCV specific CD8+ T-cells and observed a large variation in immunogenicity among 28 naturally occurring NS3(1073) variants. In this work, we intend to evaluate this immunogenic variation at molecular level, through bioinformatics approaches. The D1-EM-D2 strategy was used to build in silico MHC:peptide complexes (pMHC) of these HCV-derived peptides in the context of HLA-A*02:01 allele. The TCR-interacting surface of these complexes were evaluated using the GRASP2 program. Structural analysis indicated a sharing of topological and electrostatic features among complexes that induced strong response in vitro. Besides, complexes that induced low response presented an important positively charged spot in the center of TCR-interacting area. This spot was seen even in complexes with conservative amino acid changes and is consistent with the impairment of recognition by wild-type-specific T-cells, observed in vitro. Furthermore, the most remarkable difference in electrostatic potential was seen precisely in the only complex unable to induce in vitro stimulation. All these observations were confirmed by Principal Component Analysis (PCA) and this approach was also applied to a set of 45 non-related immunogenic viral epitopes, indicating possible new targets for cross-reactivity studies. Our results suggest structural in silico analysis of pMHC complexes as a reliable tool for vaccine development, affording to predict the impact of viral escape mutations and selection of epitopes with potential to induce cross-reactive immune responses.",
  author = "Antunes, D. A. and Rigo, M. M. and Silva, J. P. and Cibulski, S. P. and Sinigaglia, M. and Chies, J. A. and Vieira, G. F.",
  title = "{S}tructural in silico analysis of cross-genotype-reactivity among naturally occurring {H}{C}{V} {N}{S}3-1073-variants in the context of {H}{L}{A}-{A}*02:01 allele",
  journal = "Molecular Immunology",
  year = "2011",
  volume = "48",
  number = "12-13",
  pages = "1461--1467",
  month = jul
}

Abstract

Cellular immune response plays a central role in outcome of Hepatitis C Virus (HCV) infection. While specific T-cell responses are related to viral clearance, impaired responses can lead to chronic infection, turning HCV variability into a major obstacle for vaccine development. In a recent work, Fytili et al. (2008) studied the cross reactive potential of HCV specific CD8+ T-cells and observed a large variation in immunogenicity among 28 naturally occurring NS3(1073) variants. In this work, we intend to evaluate this immunogenic variation at molecular level, through bioinformatics approaches. The D1-EM-D2 strategy was used to build in silico MHC:peptide complexes (pMHC) of these HCV-derived peptides in the context of HLA-A*02:01 allele. The TCR-interacting surface of these complexes were evaluated using the GRASP2 program. Structural analysis indicated a sharing of topological and electrostatic features among complexes that induced strong response in vitro. Besides, complexes that induced low response presented an important positively charged spot in the center of TCR-interacting area. This spot was seen even in complexes with conservative amino acid changes and is consistent with the impairment of recognition by wild-type-specific T-cells, observed in vitro. Furthermore, the most remarkable difference in electrostatic potential was seen precisely in the only complex unable to induce in vitro stimulation. All these observations were confirmed by Principal Component Analysis (PCA) and this approach was also applied to a set of 45 non-related immunogenic viral epitopes, indicating possible new targets for cross-reactivity studies. Our results suggest structural in silico analysis of pMHC complexes as a reliable tool for vaccine development, affording to predict the impact of viral escape mutations and selection of epitopes with potential to induce cross-reactive immune responses.

D. A. Antunes, “In silico study of the molecular basis for cross-reactivity between viral epitopes restricted to HLA-A*02:01,” Master's thesis, Federal University of Rio Grande do Sul, Graduate Program in Genetics and Molecular Biology (PPGBM), 2011. Language: Portuguese

pdf publisher

BibTeX

@mastersthesis{Antunes-MS,
  author = "Antunes, D. A.",
  title = "In silico study of the molecular basis for cross-reactivity between viral epitopes restricted to {HLA-A}*02:01",
  school = "Federal University of Rio Grande do Sul, Graduate Program in Genetics and Molecular Biology (PPGBM)",
  year = "2011",
  month = mar,
  note = "Language: Portuguese",
  url = "https://www.lume.ufrgs.br/handle/10183/54417",
  abstract = "Recognition of the Major Histocompatibility Complex (MHC) by Cytotoxic T Lymphocytes (CTLs) is the final step of an important intracellular pathway, responsible for presenting endogenous peptides. This route allows the Immune System to perform a persistent surveillance of the cytoplasmic content of all nucleated cells, being a pivotal mechanism in antiviral and antitumoral defense. The understanding of molecular issues underlying the stimulation of a given T cell population by a specific peptide:MHC (pMHC) complex is essential for vaccine development, having special application to study the immunity against Hepatits C Virus (HCV). In a recent work, Paraskevi Fytili and colleagues evaluated the immunogenicity of an HCV-NS31073 variants subset against a CTL population previously stimulated with the wild-type epitope. Both natural and synthetic variants were used, and a large variation of IFN-gamma production by wildtype- specific T cells was observed. In this work, we intend to evaluate this variability at molecular level, through bioinformatics approaches. The prior identification of allele-specific patterns, presented by epitopes in the MHC cleft, allowed the development of a strategy for in silico construction of pMHC complexes, combining Molecular Docking and Energy Minimization (D1- EM-D2). This innovative approach was used to build 10 complexes presenting synthetic peptides and 28 complexes presenting naturally occurring variants, all in the context of human MHC allele HLA-A*02:01. The molecular surface of these complexes was further evaluated regarding its topology, electrostatic potential and Accessible Surface Area (ASA). Resulting data was used to group the variants according to its similarity with the wild-type-presenting complex, being these groups confronted with in vitro data, previously published by Fytili et al. This analysis, corroborated by multivariate statistical methods, has highlighted the sharing of structural aspects among complexes that stimulate response in vitro, as well as possible molecular issues responsible for abrogation of cellular immune response against certain HCV variants. This work suggests structural in silico analysis of pMHC complexes as a reliable tool for vaccine development, affording to predict the impact of viral escape mutations and selection of epitopes with potential to induce cross-reactive immune responses."
}

Abstract

Recognition of the Major Histocompatibility Complex (MHC) by Cytotoxic T Lymphocytes (CTLs) is the final step of an important intracellular pathway, responsible for presenting endogenous peptides. This route allows the Immune System to perform a persistent surveillance of the cytoplasmic content of all nucleated cells, being a pivotal mechanism in antiviral and antitumoral defense. The understanding of molecular issues underlying the stimulation of a given T cell population by a specific peptide:MHC (pMHC) complex is essential for vaccine development, having special application to study the immunity against Hepatits C Virus (HCV). In a recent work, Paraskevi Fytili and colleagues evaluated the immunogenicity of an HCV-NS31073 variants subset against a CTL population previously stimulated with the wild-type epitope. Both natural and synthetic variants were used, and a large variation of IFN-gamma production by wildtype- specific T cells was observed. In this work, we intend to evaluate this variability at molecular level, through bioinformatics approaches. The prior identification of allele-specific patterns, presented by epitopes in the MHC cleft, allowed the development of a strategy for in silico construction of pMHC complexes, combining Molecular Docking and Energy Minimization (D1- EM-D2). This innovative approach was used to build 10 complexes presenting synthetic peptides and 28 complexes presenting naturally occurring variants, all in the context of human MHC allele HLA-A*02:01. The molecular surface of these complexes was further evaluated regarding its topology, electrostatic potential and Accessible Surface Area (ASA). Resulting data was used to group the variants according to its similarity with the wild-type-presenting complex, being these groups confronted with in vitro data, previously published by Fytili et al. This analysis, corroborated by multivariate statistical methods, has highlighted the sharing of structural aspects among complexes that stimulate response in vitro, as well as possible molecular issues responsible for abrogation of cellular immune response against certain HCV variants. This work suggests structural in silico analysis of pMHC complexes as a reliable tool for vaccine development, affording to predict the impact of viral escape mutations and selection of epitopes with potential to induce cross-reactive immune responses.

F. S. Campos, D. Dezen, D. A. Antunes, H. F. Santos, T. S. Arantes, A. Cenci, F. Gomes, F. E. Lima, W. M. Brito, H. C. Filho, H. B. Batista, F. R. Spilki, A. C. Franco, F. A. Rijsewijk, and P. M. Roehe, “Efficacy of an inactivated, recombinant bovine herpesvirus type 5 (BoHV-5) vaccine,” Veterinary Microbiology, vol. 148, no. 1, pp. 18–26, Feb. 2011.

publisher

BibTeX

@article{Campos2011-bhv5,
  pmid = "20828945",
  doi = "10.1016/j.vetmic.2010.08.004",
  author = "Campos, F. S. and Dezen, D. and Antunes, D. A. and Santos, H. F. and Arantes, T. S. and Cenci, A. and Gomes, F. and Lima, F. E. and Brito, W. M. and Filho, H. C. and Batista, H. B. and Spilki, F. R. and Franco, A. C. and Rijsewijk, F. A. and Roehe, P. M.",
  title = "{E}fficacy of an inactivated, recombinant bovine herpesvirus type 5 ({B}o{H}{V}-5) vaccine",
  journal = "Veterinary Microbiology",
  year = "2011",
  volume = "148",
  number = "1",
  pages = "18--26",
  month = feb,
  abstract = "Bovine herpesvirus type 5 (BoHV-5) is the causative agent of bovine herpetic encephalitis. In countries where BoHV-5 is prevalent, attempts to vaccinate cattle to prevent clinical signs from BoHV-5-induced disease have relied essentially on vaccination with BoHV-1 vaccines. However, such practice has been shown not to confer full protection to BoHV-5 challenge. In the present study, an inactivated, oil adjuvanted vaccine prepared with a recombinant BoHV-5 from which the genes coding for glycoprotein I (gI), glycoprotein E (gE) and membrane protein US9 were deleted (BoHV-5 gI/gE/US9−), was evaluated in cattle in a vaccination/challenge experiment. The vaccine was prepared from a virus suspension containing a pre-inactivation antigenic mass equivalent to 10{7.69} TCID50/dose. Three mL of the inactivated vaccine were administered subcutaneously to eight calves serologically negative for BoHV-5 (vaccinated group). Four other calves were mock-vaccinated with an equivalent preparation without viral antigens (control group). Both groups were boostered 28 days later. Neither clinical signs of disease nor adverse effects were observed during or after vaccination. A specific serological response, revealed by the development of neutralizing antibodies, was detected in all vaccinated animals after the first dose of vaccine, whereas control animals remained seronegative. Calves were subsequently challenged on day 77 post-vaccination (pv) with 109.25 TCID50 of the wild-type BoHV-5 (parental strain EVI 88/95). After challenge, vaccinated cattle displayed mild signs of respiratory disease, whereas the control group developed respiratory disease and severe encephalitis, which led to culling of 2/4 calves. Searches for viral DNA in the central nervous system (CNS) of vaccinated calves indicated that wild-type BoHV-5 did not replicate, whereas in CNS tissues of calves on the control group, viral DNA was widely distributed. BoHV-5 shedding in nasal secretions was significantly lower in vaccinated calves than in the control group on days 2, 3, 4 and 6 post-challenge (pc). In addition, the duration of virus shedding was significantly shorter in the vaccinated (7 days) than in controls (12 days). Attempts to reactivate latent infection by administration of dexamethasone at 147 days pv led to recrudescence of mild signs of respiratory disease in both vaccinated and control groups. Infectious virus shedding in nasal secretions was detected at reactivation and was significantly lower in vaccinated cattle than in controls on days 11–13 post-reactivation (pr). It is concluded that the inactivated vaccine prepared with the BoHV-5 gI/gE/US9− recombinant was capable of conferring protection to encephalitis when vaccinated cattle were challenged with a large infectious dose of the parental wild type BoHV-5. However, it did not avoid the establishment of latency nor impeded dexamethasone-induced reactivation of the virus, despite a significant reduction in virus shedding after challenge and at reactivation on vaccinated calves."
}

Abstract

Bovine herpesvirus type 5 (BoHV-5) is the causative agent of bovine herpetic encephalitis. In countries where BoHV-5 is prevalent, attempts to vaccinate cattle to prevent clinical signs from BoHV-5-induced disease have relied essentially on vaccination with BoHV-1 vaccines. However, such practice has been shown not to confer full protection to BoHV-5 challenge. In the present study, an inactivated, oil adjuvanted vaccine prepared with a recombinant BoHV-5 from which the genes coding for glycoprotein I (gI), glycoprotein E (gE) and membrane protein US9 were deleted (BoHV-5 gI/gE/US9−), was evaluated in cattle in a vaccination/challenge experiment. The vaccine was prepared from a virus suspension containing a pre-inactivation antigenic mass equivalent to 107.69 TCID50/dose. Three mL of the inactivated vaccine were administered subcutaneously to eight calves serologically negative for BoHV-5 (vaccinated group). Four other calves were mock-vaccinated with an equivalent preparation without viral antigens (control group). Both groups were boostered 28 days later. Neither clinical signs of disease nor adverse effects were observed during or after vaccination. A specific serological response, revealed by the development of neutralizing antibodies, was detected in all vaccinated animals after the first dose of vaccine, whereas control animals remained seronegative. Calves were subsequently challenged on day 77 post-vaccination (pv) with 109.25 TCID50 of the wild-type BoHV-5 (parental strain EVI 88/95). After challenge, vaccinated cattle displayed mild signs of respiratory disease, whereas the control group developed respiratory disease and severe encephalitis, which led to culling of 2/4 calves. Searches for viral DNA in the central nervous system (CNS) of vaccinated calves indicated that wild-type BoHV-5 did not replicate, whereas in CNS tissues of calves on the control group, viral DNA was widely distributed. BoHV-5 shedding in nasal secretions was significantly lower in vaccinated calves than in the control group on days 2, 3, 4 and 6 post-challenge (pc). In addition, the duration of virus shedding was significantly shorter in the vaccinated (7 days) than in controls (12 days). Attempts to reactivate latent infection by administration of dexamethasone at 147 days pv led to recrudescence of mild signs of respiratory disease in both vaccinated and control groups. Infectious virus shedding in nasal secretions was detected at reactivation and was significantly lower in vaccinated cattle than in controls on days 11–13 post-reactivation (pr). It is concluded that the inactivated vaccine prepared with the BoHV-5 gI/gE/US9− recombinant was capable of conferring protection to encephalitis when vaccinated cattle were challenged with a large infectious dose of the parental wild type BoHV-5. However, it did not avoid the establishment of latency nor impeded dexamethasone-induced reactivation of the virus, despite a significant reduction in virus shedding after challenge and at reactivation on vaccinated calves.

M. M. Rigo, D. A. Antunes, M. Sinigaglia, C. C. Fülber, J. A. B. Chies, and G. F. Vieira, “Molecular aspects involved in the immunogenicity against viral epitopes: an immunoinformatic perspective,” in Immunogenicity, Nova Science Publishers, 2011, pp. 1–24.

publisher

BibTeX

@inbook{Rigo-NP10,
  author = "Rigo, M. M. and Antunes, D. A. and Sinigaglia, M. and F\"{u}lber, C. C. and Chies, J. A. B. and Vieira, G. F.",
  title = "Molecular aspects involved in the immunogenicity against viral epitopes: an immunoinformatic perspective",
  booktitle = "Immunogenicity",
  series = "Immunology and Immune System Disorders",
  pages = "1--24",
  publisher = "Nova Science Publishers",
  year = "2011",
  isbn = "978-1-61761-591-7",
  url = "http://www.novapublishers.org/catalog/product_info.php?products_id=25425",
  abstract = "Since the beginning of studies about immune response stimulation against viral infections, many pieces have already been added to complete this puzzle. Once uncovered, it will provide the key elements to the development of more efficient vaccines against a wide diversity of infectious diseases. A thorough understanding of this system involves not only the analysis of epitopes that are presented to the immune system but also the elucidation of the steps required for the generation and selection of immunogenic epitopes, such as the proteolysis mediated by proteasomes, transport associated with antigen processing (carried out by TAP protein) and binding of epitopes to the Major Histocompatibility Complex (MHC). Besides the steps in the antigen intracellular processing pathway, other aspects involved in the selection of immunogenic epitopes, such as its intrinsic avidity by the MHC cleft (on the extracellular surface) and the epitope constitution , are necessary to stimulate the T cell receptor (TCR). Certainly, the epitopes linear sequences are important; however, an epitope is much more than a simple combination of amino acids, which present a distribution of charges and topological features that actively interact with the other elements that form the MHC cleft. These elements will be essential for the induction (or not) of an immune response. In this chapter, we will discuss several aspects involved in the recognition of a viral infection.  What are the typical elements of a viral infection and how does the immune system recognize them? How could different amino acid sequences stimulate the same TCR generating cross-reactive responses? In which way can a virus infection trigger an autoimmune disease? Allied to all these immunological approaches, we intend to present how bioinformatics - or more specifically immunoinformatics - tools can be used to elucidate these processes. Thus, several tools, such as docking and molecular dynamics, peptide:MHC binding and proteasome cleavage predictors will be presented. We will also present and discuss the main molecular immunological databases, including SYFPEITHI, IEDB and IMGT. In summary, we will discuss how population, immunological, and molecular aspects are essential in the comprehension of vaccine design and autoimmunity."
}

Abstract

Since the beginning of studies about immune response stimulation against viral infections, many pieces have already been added to complete this puzzle. Once uncovered, it will provide the key elements to the development of more efficient vaccines against a wide diversity of infectious diseases. A thorough understanding of this system involves not only the analysis of epitopes that are presented to the immune system but also the elucidation of the steps required for the generation and selection of immunogenic epitopes, such as the proteolysis mediated by proteasomes, transport associated with antigen processing (carried out by TAP protein) and binding of epitopes to the Major Histocompatibility Complex (MHC). Besides the steps in the antigen intracellular processing pathway, other aspects involved in the selection of immunogenic epitopes, such as its intrinsic avidity by the MHC cleft (on the extracellular surface) and the epitope constitution , are necessary to stimulate the T cell receptor (TCR). Certainly, the epitopes linear sequences are important; however, an epitope is much more than a simple combination of amino acids, which present a distribution of charges and topological features that actively interact with the other elements that form the MHC cleft. These elements will be essential for the induction (or not) of an immune response. In this chapter, we will discuss several aspects involved in the recognition of a viral infection. What are the typical elements of a viral infection and how does the immune system recognize them? How could different amino acid sequences stimulate the same TCR generating cross-reactive responses? In which way can a virus infection trigger an autoimmune disease? Allied to all these immunological approaches, we intend to present how bioinformatics - or more specifically immunoinformatics - tools can be used to elucidate these processes. Thus, several tools, such as docking and molecular dynamics, peptide:MHC binding and proteasome cleavage predictors will be presented. We will also present and discuss the main molecular immunological databases, including SYFPEITHI, IEDB and IMGT. In summary, we will discuss how population, immunological, and molecular aspects are essential in the comprehension of vaccine design and autoimmunity.

2010

A. P. Varela, C. L. Holz, S. P. Cibulski, T. F. Teixeira, D. A. Antunes, A. C. Franco, L. R. Roehe, M. T. Oliveira, F. S. Campos, D. Dezen, A. Cenci, W. D. Brito, and P. M. Roehe, “Neutralizing antibodies to bovine herpesvirus types 1 (BoHV-1) and 5 (BoHV-5) and its subtypes,” Veterinary Microbiology, vol. 142, no. 3-4, pp. 254–260, May 2010.

publisher

BibTeX

@article{Varela2010-bhv5,
  pubmed = "19926411",
  doi = "10.1016/j.vetmic.2009.10.016",
  author = "Varela, A. P. and Holz, C. L. and Cibulski, S. P. and Teixeira, T. F. and Antunes, D. A. and Franco, A. C. and Roehe, L. R. and Oliveira, M. T. and Campos, F. S. and Dezen, D. and Cenci, A. and Brito, W. D. and Roehe, P. M.",
  title = "{N}eutralizing antibodies to bovine herpesvirus types 1 ({B}o{H}{V}-1) and 5 ({B}o{H}{V}-5) and its subtypes",
  journal = "Veterinary Microbiology",
  year = "2010",
  volume = "142",
  number = "3-4",
  pages = "254--260",
  month = may,
  abstract = "This study was carried out to determine whether the sensitivity of serum neutralization (SN) tests would be affected by the use of distinct subtypes of bovine herpesvirus 1 (BoHV-1) and 5 (BoHV-5) as test challenge viruses. Bovine sera collected from a randomized sample (n=287) were tested in a 24h incubation SN against three type 1 viruses (BoHV-1.1 strains "Los Angeles" (LA) and "EVI 123"; BoHV-1.2a strain "SV 265") and three type 5 viruses (BoHV-5a strain "EVI 88"; BoHV-5b strain "A 663" and BoHV-5c "ISO 97"). SN sensitivity varied greatly depending on the test challenge virus used in the test, particularly when results against each virus were considered individually, where it ranged from 77% (detecting 80 out of 104 antibody-positive sera) with ISO 97 to 91% (95/104) with BoHV-1.1 strain LA. All tests to single viruses revealed a significantly low sensitivity (McNemar's; p<0.05). Maximum sensitivity (104/104) was achieved when positive results to a particular combination of four of the challenge viruses (LA+EVI 123+SV 265+A 663) or some combinations of five viruses (or all six viruses) were added cumulatively. These results provide evidence for no association between any particular virus type/subtype and higher SN sensitivity. In addition, it was clearly shown that when SN is performed with single test challenge viruses, sensitivity can vary so significantly that might compromise control or eradication efforts. Performing SN against a number of different viruses demonstrated to improve significantly the test's sensitivity."
}

Abstract

This study was carried out to determine whether the sensitivity of serum neutralization (SN) tests would be affected by the use of distinct subtypes of bovine herpesvirus 1 (BoHV-1) and 5 (BoHV-5) as test challenge viruses. Bovine sera collected from a randomized sample (n=287) were tested in a 24h incubation SN against three type 1 viruses (BoHV-1.1 strains "Los Angeles" (LA) and "EVI 123"; BoHV-1.2a strain "SV 265") and three type 5 viruses (BoHV-5a strain "EVI 88"; BoHV-5b strain "A 663" and BoHV-5c "ISO 97"). SN sensitivity varied greatly depending on the test challenge virus used in the test, particularly when results against each virus were considered individually, where it ranged from 77% (detecting 80 out of 104 antibody-positive sera) with ISO 97 to 91% (95/104) with BoHV-1.1 strain LA. All tests to single viruses revealed a significantly low sensitivity (McNemar’s; p<0.05). Maximum sensitivity (104/104) was achieved when positive results to a particular combination of four of the challenge viruses (LA+EVI 123+SV 265+A 663) or some combinations of five viruses (or all six viruses) were added cumulatively. These results provide evidence for no association between any particular virus type/subtype and higher SN sensitivity. In addition, it was clearly shown that when SN is performed with single test challenge viruses, sensitivity can vary so significantly that might compromise control or eradication efforts. Performing SN against a number of different viruses demonstrated to improve significantly the test’s sensitivity.

D. A. Antunes, G. F. Vieira, M. M. Rigo, S. P. Cibulski, M. Sinigaglia, and J. A. Chies, “Structural allele-specific patterns adopted by epitopes in the MHC-I cleft and reconstruction of MHC:peptide complexes to cross-reactivity assessment,” PLoS ONE, vol. 5, no. 4, p. e10353, Apr. 2010.

pdf publisher

BibTeX

@article{antunes2010-allele-pattern,
  doi = "10.1371/journal.pone.0010353",
  author = "Antunes, D. A. and Vieira, G. F. and Rigo, M. M. and Cibulski, S. P. and Sinigaglia, M. and Chies, J. A.",
  title = "{S}tructural allele-specific patterns adopted by epitopes in the {M}{H}{C}-{I} cleft and reconstruction of {M}{H}{C}:peptide complexes to cross-reactivity assessment",
  journal = "PLoS ONE",
  year = "2010",
  volume = "5",
  number = "4",
  pages = "e10353",
  month = apr,
  abstract = "The immune system is engaged in a constant antigenic surveillance through the Major Histocompatibility Complex (MHC) class I antigen presentation pathway. This is an efficient mechanism for detection of intracellular infections, especially viral ones. In this work we describe conformational patterns shared by epitopes presented by a given MHC allele and use these features to develop a docking approach that simulates the peptide loading into the MHC cleft. Our strategy, to construct in silico MHC:peptide complexes, was successfully tested by reproducing four different crystal structures of MHC-I molecules available at the Protein Data Bank (PDB). An in silico study of cross-reactivity potential was also performed between the wild-type complex HLA-A2-NS31073 and nine MHC:peptide complexes presenting alanine exchange peptides. This indicates that structural similarities among the complexes can give us important clues about cross reactivity. The approach used in this work allows the selection of epitopes with potential to induce cross-reactive immune responses, providing useful tools for studies in autoimmunity and to the development of more comprehensive vaccines."
}

Abstract

The immune system is engaged in a constant antigenic surveillance through the Major Histocompatibility Complex (MHC) class I antigen presentation pathway. This is an efficient mechanism for detection of intracellular infections, especially viral ones. In this work we describe conformational patterns shared by epitopes presented by a given MHC allele and use these features to develop a docking approach that simulates the peptide loading into the MHC cleft. Our strategy, to construct in silico MHC:peptide complexes, was successfully tested by reproducing four different crystal structures of MHC-I molecules available at the Protein Data Bank (PDB). An in silico study of cross-reactivity potential was also performed between the wild-type complex HLA-A2-NS31073 and nine MHC:peptide complexes presenting alanine exchange peptides. This indicates that structural similarities among the complexes can give us important clues about cross reactivity. The approach used in this work allows the selection of epitopes with potential to induce cross-reactive immune responses, providing useful tools for studies in autoimmunity and to the development of more comprehensive vaccines.

C. C. Fülber, D. A. Antunes, M. M. Rigo, J. A. Chies, M. Sinigaglia, and G. F. Vieira, “Reconstruction of MHC Alleles by Cross Modeling and Structural Assessment,” in International Conference on Bioinformatics and Computational Biology (BIOCOMP’10), 2010, pp. 459–463. Las Vegas, United States (Short-Article)

BibTeX

@inproceedings{Fulber2010-BIOCOMP,
  author = "Fülber, C. C. and Antunes, D. A. and Rigo, M. M. and Chies, J. A. and Sinigaglia, M. and Vieira, G. F.",
  title = "Reconstruction of MHC Alleles by Cross Modeling and Structural Assessment",
  booktitle = "International Conference on Bioinformatics and Computational Biology (BIOCOMP'10)",
  year = "2010",
  pages = "459-463",
  note = "Las Vegas, United States (Short-Article)"
}

2009

M. M. Rigo, D. A. Antunes, G. F. Vieira, and J. A. Chies, “MHC:Peptide Analysis: Implications on the Immunogenicity of Hantaviruses’ N protein,” in Advances in Bioinformatics and Computational Biology - BSB 2009, 2009, pp. 160–163. Porto Alegre, Brazil (Short-Article)

publisher

BibTeX

@inproceedings{Rigo2009,
  doi = "10.1007/978-3-642-03223-3_17",
  author = "Rigo, M. M. and Antunes, D. A. and Vieira, G. F. and Chies, J. A.",
  title = "{MHC:Peptide} Analysis: Implications on the Immunogenicity of Hantaviruses' N protein",
  booktitle = "Advances in Bioinformatics and Computational Biology - BSB 2009",
  year = "2009",
  pages = "160--163",
  publisher = "Springer, Berlin, Heidelberg",
  series = "Lecture Notes in Computer Science",
  editor = "Guimar\~{a}es K.S., Panchenko A., Przytycka T.M",
  note = "Porto Alegre, Brazil (Short-Article)"
}

2008

D. A. Antunes, “Use of bioinformatics tools for the analysis of cross-reactivity likelihood between viral epitopes,” Undergraduate thesis, Federal University of Rio Grande do Sul, Institute of Basic Health Sciences, 2008. Language: Portuguese

pdf publisher

BibTeX

@mastersthesis{Antunes-BS,
  author = "Antunes, D. A.",
  title = "Use of bioinformatics tools for the analysis of cross-reactivity likelihood between viral epitopes",
  school = "Federal University of Rio Grande do Sul, Institute of Basic Health Sciences",
  year = "2008",
  month = aug,
  type = "{U}ndergraduate thesis",
  note = "Language: Portuguese",
  url = "https://www.lume.ufrgs.br/handle/10183/28693",
  abstract = "A reatividade cruzada é definida como a capacidade de um linfócito T em reconhecer, no contexto do Complexo Principal de Histocompatibilidade (MHC), peptídeos não relacionados, provenientes de um mesmo organismo ou mesmo de organismos heterólogos. Este fenômeno já foi descrito em inúmeros trabalhos, embora os mecanismos que permitam este reconhecimento cruzado ainda não tenham sido completamente estabelecidos. O reconhecimento do complexo MHC:peptídeo (pMHC) pelo Receptor de Célula T (TCR) leva à lise da célula apresentadora, o que torna a reatividade cruzada alvo de profundo interesse em estudos que envolvem os mecanismos citotóxicos da resposta imune. Neste trabalho realizamos um estudo in silico da possível ocorrência de reatividade cruzada entre os epitopos virais PA224-233 (Influenza) e HBsAg28-39 (HBV) no contexto do MHC murino H-2Db. O complexo H-2Db:PA224-233 (DbPA224-233) possue estrutura depositada no Protein Data Bank (PDB) sob o código de acesso 1WBY, enquanto a estrutura do complexo DbHBsAg28-39 ainda não foi determinada. Utilizando o programa AutoDock 4 para realizar o docking do epitopo ao MHC e o pacote GROMACS para realizar a minimização de energia (EM) das estruturas geradas, conseguimos construir o complexo DbHBsAg30-39. O programa GRASP2 foi utilizado para as análises de topologia e distribuição de cargas do complexo. A comparação entre a estrutura 1WBY e o complexo gerado indicou forte correlação estrutural, contudo identificamos uma diferença de cargas em uma posição crítica para o reconhecimento pelo TCR. Para contornar esta diferença, construímos um epitopo de Influenza mutado (R7W) e repetimos as análises. Este novo peptídeo apresentou alta afinidade pelo MHC e maior semelhança com o epitopo de HBV, possivelmente induzindo reatividade cruzada. A estratégia desenvolvida para a realização deste trabalho pode ser utilizada para simular complexos pMHC formados com qualquer epitopo e o estudo de topologia/potencial do complexo formado nos permite comparar dois diferentes complexos pMHC sob o ponto de vista do TCR, possibilitando discutir reatividade cruzada in silico. Em conjunto, estas técnicas apresentam grande potencial de aplicação no estudo de patologias autoimunes e no desenvolvimento de vacinas antivirais de amplo espectro."
}

Abstract

A reatividade cruzada é definida como a capacidade de um linfócito T em reconhecer, no contexto do Complexo Principal de Histocompatibilidade (MHC), peptídeos não relacionados, provenientes de um mesmo organismo ou mesmo de organismos heterólogos. Este fenômeno já foi descrito em inúmeros trabalhos, embora os mecanismos que permitam este reconhecimento cruzado ainda não tenham sido completamente estabelecidos. O reconhecimento do complexo MHC:peptídeo (pMHC) pelo Receptor de Célula T (TCR) leva à lise da célula apresentadora, o que torna a reatividade cruzada alvo de profundo interesse em estudos que envolvem os mecanismos citotóxicos da resposta imune. Neste trabalho realizamos um estudo in silico da possível ocorrência de reatividade cruzada entre os epitopos virais PA224-233 (Influenza) e HBsAg28-39 (HBV) no contexto do MHC murino H-2Db. O complexo H-2Db:PA224-233 (DbPA224-233) possue estrutura depositada no Protein Data Bank (PDB) sob o código de acesso 1WBY, enquanto a estrutura do complexo DbHBsAg28-39 ainda não foi determinada. Utilizando o programa AutoDock 4 para realizar o docking do epitopo ao MHC e o pacote GROMACS para realizar a minimização de energia (EM) das estruturas geradas, conseguimos construir o complexo DbHBsAg30-39. O programa GRASP2 foi utilizado para as análises de topologia e distribuição de cargas do complexo. A comparação entre a estrutura 1WBY e o complexo gerado indicou forte correlação estrutural, contudo identificamos uma diferença de cargas em uma posição crítica para o reconhecimento pelo TCR. Para contornar esta diferença, construímos um epitopo de Influenza mutado (R7W) e repetimos as análises. Este novo peptídeo apresentou alta afinidade pelo MHC e maior semelhança com o epitopo de HBV, possivelmente induzindo reatividade cruzada. A estratégia desenvolvida para a realização deste trabalho pode ser utilizada para simular complexos pMHC formados com qualquer epitopo e o estudo de topologia/potencial do complexo formado nos permite comparar dois diferentes complexos pMHC sob o ponto de vista do TCR, possibilitando discutir reatividade cruzada in silico. Em conjunto, estas técnicas apresentam grande potencial de aplicação no estudo de patologias autoimunes e no desenvolvimento de vacinas antivirais de amplo espectro.

M. M. Rigo, D. A. Antunes, G. F. Vieira, and J. A. Chies, “Immunogenic regions on the N protein from hantavirus genus: implications in vaccine development,” in Anals of the 4th International Conference of the Brazilian Association for Bioinformatics and Computational Biology (X-Meeting 2008), 2008. Salvador, Brazil (Short-Article)

BibTeX

@inproceedings{Rigo-XM8,
  author = "Rigo, M. M. and Antunes, D. A. and Vieira, G. F. and Chies, J. A.",
  title = "Immunogenic regions on the N protein from hantavirus genus: implications in vaccine development",
  booktitle = "Anals of the 4th International Conference of the Brazilian Association for Bioinformatics and Computational Biology (X-Meeting 2008)",
  year = "2008",
  note = "Salvador, Brazil (Short-Article)"
}

2007

D. A. Antunes, G. F. Vieira, and J. A. Chies, “Structural analyses of viral epitopes and proteolytic simulation of its proteins,” in Anals of 3rd International Conference of the Brazilian Association for Bioinformatics and Computational Biology (X-Meeting 2007), 2007. São Paulo, Brazil (Short-Article)

BibTeX

@inproceedings{Antunes-XM7,
  author = "Antunes, D. A. and Vieira, G. F. and Chies, J. A.",
  title = "Structural analyses of viral epitopes and proteolytic simulation of its proteins",
  booktitle = "Anals of 3rd International Conference of the Brazilian Association for Bioinformatics and Computational Biology (X-Meeting 2007)",
  year = "2007",
  note = "S\~{a}o Paulo, Brazil (Short-Article)"
}

G. F. Vieira, D. A. Antunes, and J. A. Chies, “Viral epitopes: which is(are) the target(s)?,” in Anals of 3rd International Conference of the Brazilian Association for Bioinformatics and Computational Biology (X-Meeting 2007), 2007. São Paulo, Brazil (Short-Article)

BibTeX

@inproceedings{Vieira-XM7,
  author = "Vieira, G. F. and Antunes, D. A. and Chies, J. A.",
  title = "Viral epitopes: which is(are) the target(s)?",
  booktitle = "Anals of 3rd International Conference of the Brazilian Association for Bioinformatics and Computational Biology (X-Meeting 2007)",
  year = "2007",
  note = "S\~{a}o Paulo, Brazil (Short-Article)"
}

Dinler A. Antunes

2025

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

2024

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

2023

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

2022

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

2021

BibTeX

Abstract

2020

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

2019

BibTeX

Abstract

BibTeX

Abstract

BibTeX

Abstract

2018

BibTeX

Abstract

BibTeX