Cited by 26vaccines and immunoinformatics approachesProtein Structure and DynamicsChemical Synthesis and Analysis

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes

Open access
Gupta, Sagar, Nerli, Santrupti, Kutti Kandy, Sreeja, Mersky, Glenn L., Sgourakis, Nikolaos G.·2023·Nature Communications

Published version of a preprint

Abstract

The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptides bound to the human MHC, HLA, has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within our curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer pHLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work may be applied towards predicting antigen immunogenicity, and receptor cross-reactivity.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-023-42163-z

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 5 references

Research works printed but not linked: 83

  1. Rock, K. L., Reits, E. & Neefjes, J. Present yourself! By MHC class I and MHC class II molecules. Trends Immunol. 37, 724–737 (2016). · DOI · Paper page
  2. Mungall, A. J. et al. The DNA sequence and analysis of human chromosome 6. Nature 425, 805–811 (2003). · DOI
  3. Wieczorek, M. et al. Major histocompatibility complex (MHC) class I and MHC class II proteins: conformational plasticity in antigen presentation. Front. Immunol. 8, 292 (2017). · DOI
  4. Barker, D. J. et al. The IPD-IMGT/HLA database. Nucleic Acids Res. 51, D1053–D1060 (2023). · DOI
  5. Gfeller, D. & Bassani-Sternberg, M. Predicting antigen presentation—what could we learn from a million peptides? Front. Immunol. 9. https://doi.org/10.3389/fimmu.2018.01716 (2018). · DOI
  6. Petersdorf, E. W. HLA matching in allogeneic stem cell transplantation. Curr. Opin. Hematol. 11, 386–391 (2004). · DOI
  7. Nguyen, A. T., Szeto, C. & Gras, S. The pockets guide to HLA class I molecules. Biochem. Soc. Trans. 49, 2319–2331 (2021). · DOI
  8. Josephs, T. M., Grant, E. J. & Gras, S. Molecular challenges imposed by MHC-I restricted long epitopes on T cell immunity. Biol. Chem. 398, 1027–1036 (2017). · DOI
  9. Jurtz, V. et al. NetMHCpan-4.0: improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199, 3360–3368 (2017). · DOI
  10. Riley, T. P. et al. Structure based prediction of neoantigen immunogenicity. Front. Immunol. 10. https://doi.org/10.3389/fimmu.2019.02047 (2019). · DOI
  11. Nerli, S. & Sgourakis, N. G. Structure-based modeling of SARS-CoV-2 Peptide/HLA-A02 antigens. Front. Med. Technol. 2. https://doi.org/10.3389/fmedt.2020.553478 (2020). · DOI
  12. Toor, J. S. et al. A recurrent mutation in anaplastic lymphoma kinase with distinct neoepitope conformations. Front. Immunol. 9. https://doi.org/10.3389/fimmu.2018.00099 (2018). · DOI
  13. Bjorkman, P. J., Strominger, J. L. & Wiley, D. C. Structure of the human class I histocompatibility antigen, HLA-A2. 7. Nature 329, 506–512 (1987). · DOI
  14. Berman, H. M. et al. The protein data bank. Nucleic Acids Res. 28, 235–242 (2000). · DOI · Paper page
  15. Antunes, D. A., Abella, J. R., Devaurs, D., Rigo, M. M. & Kavraki, L. E. Structure-based methods for binding mode and binding affinity prediction for peptide-MHC complexes. Curr. Top. Med. Chem. 18, 2239–2255 (2018). · DOI
  16. Antunes, D. A. et al. Structural allele-specific patterns adopted by epitopes in the MHC-I cleft and reconstruction of MHC:peptide complexes to cross-reactivity assessment. PLoS One 5, e10353 (2010). · DOI
  17. Khan, J. M. & Ranganathan, S. pDOCK: a new technique for rapid and accurate docking of peptide ligands to Major Histocompatibility Complexes. Immunome Res. 6, S2 (2010).
  18. Liu, T. et al. Subangstrom accuracy in pHLA-I modeling by Rosetta FlexPepDock refinement protocol. J. Chem. Inf. Model. 54, 2233–2242 (2014). · DOI
  19. Menegatti Rigo, M. et al. DockTope: a Web-based tool for automated pMHC-I modelling. Sci. Rep. 5, 18413 (2015). · DOI
  20. Todman, S. J. et al. Toward the atomistic simulation of T cell epitopes automated construction of MHC: peptide structures for free energy calculations. J. Mol. Graph Model 26, 957–961 (2008). · DOI
  21. Yanover, C. & Bradley, P. Large-scale characterization of peptide-MHC binding landscapes with structural simulations. Proc. Natl Acad. Sci. USA 108, 6981–6986 (2011). · DOI
  22. Bordner, A. J. & Abagyan, R. Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes. Proteins 63, 512–526 (2006). · DOI
  23. Bui, H.-H., Schiewe, A. J., von Grafenstein, H. & Haworth, I. S. Structural prediction of peptides binding to MHC class I molecules. Proteins 63, 43–52 (2006). · DOI
  24. Kyeong, H.-H., Choi, Y. & Kim, H.-S. GradDock: rapid simulation and tailored ranking functions for peptide-MHC Class I docking. Bioinformatics 34, 469–476 (2018). · DOI
  25. Marzella, D. F. et al. PANDORA: a fast, anchor-restrained modelling protocol for peptide: MHC complexes. Front. Immunol. 13, 878762 (2022). · DOI
  26. Rognan, D., Lauemoller, S. L., Holm, A., Buus, S. & Tschinke, V. Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins. J. Med. Chem. 42, 4650–4658 (1999). · DOI
  27. Tong, J. C., Tan, T. W. & Ranganathan, S. Modeling the structure of bound peptide ligands to major histocompatibility complex. Protein Sci. 13, 2523–2532 (2004). · DOI
  28. Antes, I., Siu, S. W. I. & Lengauer, T. DynaPred: a structure and sequence based method for the prediction of MHC class I binding peptide sequences and conformations. Bioinformatics 22, e16–e24 (2006). · DOI
  29. Keller, G. L. J., Weiss, L. I. & Baker, B. M. Physicochemical heuristics for identifying high fidelity, near-native structural models of Peptide/MHC complexes. Front. Immunol. 13, 887759 (2022). · DOI
  30. Rajeshwar, R. & Smith, J. C. Structural patterns in class 1 major histocompatibility complex-restricted nonamer peptide binding to T-cell receptors. Proteins https://doi.org/10.1002/prot.26343 (2022). · DOI
  31. Rudolph, M. G., Stanfield, R. L. & Wilson, I. A. How TCRs bind MHCs, peptides, and coreceptors. Annu. Rev. Immunol. 24, 419–466 (2006). · DOI
  32. Cole, D. K. et al. Hotspot autoimmune T cell receptor binding underlies pathogen and insulin peptide cross-reactivity. J. Clin. Invest. 126, 2191–2204 (2016). · DOI
  33. Abella, J. R., Antunes, D. A., Clementi, C. & Kavraki, L. E. APE-Gen: a fast method for generating ensembles of bound peptide-MHC conformations. Molecules 24, E881 (2019). · DOI
  34. Motmaen, A. et al. Peptide binding specificity prediction using fine-tuned protein structure prediction networks. Proc. Natl Acad. Sci. USA 120, e2216697120 (2023). · DOI · Paper page
  35. Antunes, D. A. et al. Interpreting T-cell cross-reactivity through structure: implications for TCR-based cancer immunotherapy. Front. Immunol. 8, 1210 (2017). · DOI
  36. Hellman, L. M. et al. Improving T cell receptor on-target specificity via structure-guided design. Mol. Ther. 27, 300–313 (2019). · DOI
  37. Mendes, M. F. et al. MatchTope: a tool to predict the cross reactivity of peptides complexed with Major Histocompatibility Complex I. Front. Immunol. 13, 930590 (2022). · DOI
  38. Mendes, M. F. A., Antunes, D. A., Rigo, M. M., Sinigaglia, M. & Vieira, G. F. Improved structural method for T-cell cross-reactivity prediction. Mol. Immunol. 67, 303–310 (2015). · DOI
  39. Schmidt, J. et al. Prediction of neo-epitope immunogenicity reveals TCR recognition determinants and provides insight into immunoediting. Cell Rep. Med. 2, 100194 (2021). · DOI
  40. Tarabini, R. F. et al. Large-scale structure-based screening of potential T cell cross-reactivities involving peptide-targets from BCG vaccine and SARS-CoV-2. Front. Immunol. 12, 812176 (2022). · DOI
  41. Menezes Teles e Oliveira, D. et al. pHLA3D: an online database of predicted three-dimensional structures of HLA molecules. Hum. Immunol. 80, 834–841 (2019). · DOI
  42. Ehrenmann, F., Kaas, Q. & Lefranc, M.-P. IMGT/3Dstructure-DB and IMGT/DomainGapAlign: a database and a tool for immunoglobulins or antibodies, T cell receptors, MHC, IgSF and MhcSF. Nucleic Acids Res. 38, D301–D307 (2010). · DOI
  43. Ehrenmann, F. & Lefranc, M.-P. IMGT/3Dstructure-DB: querying the IMGT database for 3D structures in immunology and immunoinformatics (IG or Antibodies, TR, MH, RPI, and FPIA). Cold Spring Harb. Protoc. 2011, pdb.prot5637 (2011). · DOI
  44. Kaas, Q., Ruiz, M. & Lefranc, M.-P. IMGT/3Dstructure-DB and IMGT/StructuralQuery, a database and a tool for immunoglobulin, T cell receptor and MHC structural data. Nucleic Acids Res. 32, D208–D210 (2004). · DOI
  45. Khan, J. M., Cheruku, H. R., Tong, J. C. & Ranganathan, S. MPID-T2: a database for sequence–structure–function analyses of pMHC and TR/pMHC structures. Bioinformatics 27, 1192–1193 (2011). · DOI
  46. Tong, J. C., Kong, L., Tan, T. W. & Ranganathan, S. MPID-T: database for sequence-structure-function information on T-cell receptor/peptide/MHC interactions. Appl Bioinforma. 5, 111–114 (2006). · DOI
  47. Teles e Oliveira, D. M. et al. pHLA3D: updating the database of predicted three-dimensional structures of HLA with HLA-DR, HLA-DQ and HLA-DP molecules. Hum. Immunol. 82, 8–10 (2021). · DOI
  48. Sinigaglia, M., Antunes, D. A., Rigo, M. M., Chies, J. A. B. & Vieira, G. F. CrossTope: a curate repository of 3D structures of immunogenic peptide: MHC complexes. Database (Oxf.) 2013, bat002 (2013).
  49. Li, X. et al. HLA3D: an integrated structure-based computational toolkit for immunotherapy. Brief Bioinform 23, bbac076 (2022). · DOI
  50. Sidney, J., Peters, B., Frahm, N., Brander, C. & Sette, A. HLA class I supertypes: a revised and updated classification. BMC Immunol. 9, 1 (2008). · DOI · Paper page
  51. Trolle, T. et al. The length distribution of class I restricted T cell epitopes is determined by both peptide supply and MHC allele specific binding preference. J. Immunol. 196, 1480–1487 (2016). · DOI
  52. Adzhubei, A. A., Sternberg, M. J. E. & Makarov, A. A. Polyproline-II Helix in Proteins: Structure and Function. J. Mol. Biol. 425, 2100–2132 (2013). · DOI
  53. Kufareva, I. & Abagyan, R. Methods of protein structure comparison. Methods Mol. Biol. 857, 231–257 (2012). · DOI
  54. Huang, P.-S. et al. RosettaRemodel: a generalized framework for flexible backbone protein design. PLoS One 6, e24109 (2011). · DOI
  55. Liu, Y. & Kuhlman, B. RosettaDesign server for protein design. Nucleic Acids Res. 34, W235–W238 (2006). · DOI
  56. Kim, Y., Sidney, J., Pinilla, C., Sette, A. & Peters, B. Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior. BMC Bioinforma. 10, 394 (2009).
  57. Das, R. Four small puzzles that Rosetta doesn’t solve. PLoS One 6, e20044 (2011). · DOI
  58. Vita, R. et al. The immune epitope database (IEDB): 2018 update. Nucleic Acids Res. 47, D339–D343 (2019). · DOI
  59. Chersi, A., di Modugno, F. & Rosano, L. Flexibility of amino acid residues at position four of nonapeptides enhances their binding to human leucocyte antigen (HLA) molecules. Z. Naturforsch. C. J. Biosci. 55, 109–114 (2000). · DOI
  60. Strønen, E. et al. Targeting of cancer neoantigens with donor-derived T cell receptor repertoires. Science 352, 1337–1341 (2016). · DOI
  61. Allen, T. M. et al. Selective escape from CD8+ T-cell responses represents a major driving force of human immunodeficiency virus type 1 (HIV-1) sequence diversity and reveals constraints on HIV-1 evolution. J. Virol. 79, 13239–13249 (2005). · DOI
  62. Kelow, S. P., Adolf-Bryfogle, J. & Dunbrack, R. L. Hiding in plain sight: structure and sequence analysis reveals the importance of the antibody DE loop for antibody-antigen binding. mAbs 12, 1840005 (2020). · DOI
  63. Kelow, S. et al. A penultimate classification of canonical antibody CDR conformations. 2022.10.12.511988 Preprint at https://doi.org/10.1101/2022.10.12.511988 (2022). · DOI
  64. Shapovalov, M., Vucetic, S. & Dunbrack, R. L. Jr. A new clustering and nomenclature for beta turns derived from high-resolution protein structures. PLOS Comput. Biol. 15, e1006844 (2019). · DOI
  65. Parker, M. I., Meyer, J. E., Golemis, E. A. & Dunbrack, R. L. Jr. Delineating the RAS conformational landscape. Cancer Res. 82, 2485–2498 (2022). · DOI · Paper page
  66. Fodor, J., Riley, B. T., Borg, N. A. & Buckle, A. M. Previously hidden dynamics at the TCR–peptide–MHC interface revealed. J. Immunol. 200, 4134–4145 (2018). · DOI
  67. Ayres, C. M., Corcelli, S. A. & Baker, B. M. Peptide and peptide-dependent motions in MHC proteins: immunological implications and biophysical underpinnings. Front. Immunol. 8, 935 (2017). · DOI
  68. Riley, T. P. et al. T cell receptor cross-reactivity expanded by dramatic peptide–MHC adaptability. Nat. Chem. Biol. 14, 934–942 (2018). · DOI
  69. Chandran, S. S. et al. Immunogenicity and therapeutic targeting of a public neoantigen derived from mutated PIK3CA. Nat. Med. 28, 946–957 (2022). · DOI
  70. Borrman, T., Pierce, B. G., Vreven, T., Baker, B. M. & Weng, Z. High-throughput modeling and scoring of TCR-pMHC complexes to predict cross-reactive peptides. Bioinformatics 36, 5377–5385 (2020). · DOI
  71. Bradley, P. Structure-based prediction of T cell receptor:peptide-MHC interactions. Elife 12, e82813 (2023). · DOI · Paper page
  72. Jensen, K. K. et al. TCRpMHCmodels: structural modelling of TCR-pMHC class I complexes. Sci. Rep. 9, 14530 (2019).
  73. Montemurro, A. et al. NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data. Commun. Biol. 4, 1–13 (2021). · DOI
  74. Papadaki, G. F. et al. Decoupling peptide binding from T cell receptor recognition with engineered chimeric MHC-I molecules. Front. Immunol. 14, 1116906 (2023). · DOI
  75. Yarmarkovich, M. et al. Cross-HLA targeting of intracellular oncoproteins with peptide-centric CARs. Nature 599, 477–484 (2021). · DOI
  76. Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009). · DOI · Paper page
  77. Robinson, J. et al. IPD-IMGT/HLA database. Nucleic Acids Res 48, D948–D955 (2020).
  78. Chaudhury, S., Lyskov, S. & Gray, J. J. PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta. Bioinformatics 26, 689–691 (2010). · DOI
  79. Leaver-Fay, A., Snoeyink, J. & Kuhlman, B. On-the-fly rotamer pair energy evaluation in protein design. in Bioinformatics Research and Applications (eds Măndoiu, I., Sunderraman, R. & Zelikovsky, A.) 343–354 (Springer, 2008). https://doi.org/10.1007/978-3-540-79450-9_32. · DOI
  80. Nivón, L. G., Moretti, R. & Baker, D. A Pareto-optimal refinement method for protein design scaffolds. PLoS One 8, e59004 (2013). · DOI
  81. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020). · DOI · Paper page
  82. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
  83. Thomsen, M. C. F. & Nielsen, M. Seq2Logo: a method for construction and visualization of amino acid binding motifs and sequence profiles including sequence weighting, pseudo counts and two-sided representation of amino acid enrichment and depletion. Nucleic Acids Res. 40, W281–W287 (2012). · DOI

Metadata

Authors
Gupta, S., Nerli, S., Kutti Kandy, S., Mersky, G. L., & Sgourakis, N. G.
Affiliations
Children's Hospital of Philadelphia (US)
Corresponding author
Sgourakis, Nikolaos G.
Venue
Nature Communications
Published
2023-10-10
Volume
14
Issue
1
Pages
6349-6349
ISSN
2041-1723 (ISSN-L)
OpenAlex
W4387491415
Cited by
26
References
5
Citations by year
2026: 5 · 2025: 6 · 2024: 10 · 2023: 5
FWCI
2.31
Type
journal article
Language
en
Open access
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (5R35GM125034)
  • Division of Intramural Research, National Institute of Allergy and Infectious Diseases (5R01AI143997)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
Keywords
peptide-HLA complex, T cell epitopes, MHC class I, homology modeling, peptide backbone conformation, HLA allotypes, X-ray crystal structure, antigen immunogenicity, receptor cross-reactivity
MeSH terms
Histocompatibility Antigens, Humans, Peptides, Receptors, Antigen, T-Cell, Histocompatibility Antigens Class I, Epitopes, T-Lymphocyte

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review