Abstract
T cells are essential immune cells responsible for identifying and eliminating pathogens. Through interactions between their T-cell antigen receptors (TCRs) and antigens presented by major histocompatibility complex molecules (MHCs) or MHC-like molecules, T cells discriminate foreign and self peptides. Determining the fundamental principles that govern these interactions has important implications in numerous medical contexts. However, reconstructing a map between T cells and their antagonist antigens remains an open challenge for the field of immunology, and success of in silico reconstructions of this relationship has remained incremental. In this Perspective, we discuss the role that new state-of-the-art deep-learning models for predicting protein structure may play in resolving some of the unanswered questions the field faces linking TCR and peptide–MHC properties to T-cell specificity. We provide a comprehensive overview of structural databases and the evolution of predictive models, and highlight the breakthrough AlphaFold provided the field.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Vignali, D. A. A., Collison, L. W. & Workman, C. J. How regulatory T cells work. Nat. Rev. Immunol. 8, 523–532 (2008).
Godfrey, D. I., Uldrich, A. P., McCluskey, J., Rossjohn, J. & Moody, D. B. The burgeoning family of unconventional T cells. Nat. Immunol. 16, 1114–1123 (2015).
Turner, S. J., Doherty, P. C., McCluskey, J. & Rossjohn, J. Structural determinants of T-cell receptor bias in immunity. Nat. Rev. Immunol. 6, 883–894 (2006).
Robinson, J. et al. The IMGT/HLA database. Nucleic Acids Res. 41, D1222–D1227 (2013).
Bjorkman, P. J. et al. Structure of the human class I histocompatibility antigen, HLA-A2. Nature 329, 506–512 (1987).
Rudolph, M. G., Stanfield, R. L. & Wilson, I. A. How TCRs bind MHCs, peptides, and coreceptors. Annu. Rev. Immunol. 24, 419–466 (2006). A seminal review of the interactions between TCRs and cognate pMHCs, based on the initial crystallography structures of these molecules.
Blum, J. S., Wearsch, P. A. & Cresswell, P. Pathways of antigen processing. Annu. Rev. Immunol. 31, 443–473 (2013).
Karamooz, E., Harriff, M. J. & Lewinsohn, D. M. MR1-dependent antigen presentation. Semin. Cell Dev. Biol. 84, 58–64 (2018).
Zajonc, D. M. The CD1 family: serving lipid antigens to T cells since the Mesozoic era. Immunogenetics 68, 561–576 (2016).
Buckley, P. R., Lee, C. H., Antanaviciute, A., Simmons, A. & Koohy, H. A systems approach evaluating the impact of SARS-CoV-2 variant of concern mutations on CD8+ T cell responses. Immunother. Adv. 3, ltad005 (2023).
Mason, D. A very high level of crossreactivity is an essential feature of the T-cell receptor. Immunol. Today 19, 395–404 (1998).
Sewell, A. K. Why must T cells be cross-reactive? Nat. Rev. Immunol. 12, 669–677 (2012). Arguments as to why T cells are cross-reactive by evolutionary design.
Cameron, B. J. et al. Identification of a Titin-derived HLA-A1-presented peptide as a cross-reactive target for engineered MAGE A3-directed T cells. Sci. Transl. Med. 5, 197ra103 (2013).
Linette, G. P. et al. Cardiovascular toxicity and titin cross-reactivity of affinity-enhanced T cells in myeloma and melanoma. Blood 122, 863–871 (2013).
Hudson, D., Fernandes, R. A., Basham, M., Ogg, G. & Koohy, H. Can we predict T cell specificity with digital biology and machine learning? Nat. Rev. Immunol. 23, 511–521 (2023). Overview of current experimental and computational approaches to understanding and predicting T-cell specificity.
Grazioli, F. et al. On TCR binding predictors failing to generalize to unseen peptides. Front. Immunol. 13, 1014256 (2022).
Moris, P. et al. Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification. Brief. Bioinform. 22, bbaa318 (2021).
Deng, L. et al. Performance comparison of TCR-pMHC prediction tools reveals a strong data dependency. Front. Immunol. https://doi.org/10.3389/fimmu.2023.1128326 (2023).
Gras, S. et al. Reversed T cell receptor docking on a major histocompatibility class I complex limits involvement in the immune response. Immunity 45, 749–760 (2016).
Beringer, D. X. et al. T cell receptor reversed polarity recognition of a self-antigen major histocompatibility complex. Nat. Immunol. 16, 1153–1161 (2015).
Hahn, M., Nicholson, M. J., Pyrdol, J. & Wucherpfennig, K. W. Unconventional topology of self peptide-major histocompatibility complex binding by a human autoimmune T cell receptor. Nat. Immunol. 6, 490–496 (2005).
Deseke, M. & Prinz, I. Ligand recognition by the γδ TCR and discrimination between homeostasis and stress conditions. Cell. Mol. Immunol. 17, 914–924 (2020).
Wegrecki, M. et al. Atypical sideways recognition of CD1a by autoreactive γδ T cell receptors. Nat. Commun. 13, 3872 (2022).
Zareie, P. et al. Canonical T cell receptor docking on peptide–MHC is essential for T cell signaling. Science 372, eabe9124 (2021).
Zhao, Y. et al. DeepAIR: a deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis. Sci. Adv. 9, eabo5128 (2023).
Lanzarotti, E., Marcatili, P. & Nielsen, M. T-cell receptor cognate target prediction based on paired α and β chain sequence and structural CDR loop similarities. Front. Immunol. 10, 2080 (2019).
Amaya-Ramirez, D., Martinez-Enriquez, L. C. & Parra-López, C. Usefulness of docking and molecular dynamics in selecting tumor neoantigens to design personalized cancer vaccines: a proof of concept. Vaccines (Basel) 11, 1174 (2023).
Sušac, L. et al. Structure of a fully assembled tumor-specific T cell receptor ligated by pMHC. Cell 185, 3201–3213(2022).
Riley, T. P. et al. A generalized framework for computational design and mutational scanning of T-cell receptor binding interfaces. Protein Eng. Des. Sel. 29, 595–606 (2016).
Borrman, T. et al. ATLAS: a database linking binding affinities with structures for wild-type and mutant TCR–pMHC complexes. Proteins85, 908–916 (2017). Database linking binding affinity data to TCR–pMHC complex structures.
Hellman, L. M. et al. Improving T cell receptor on-target specificity via structure-guided design. Mol. Ther. 27, 300–313 (2019).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). Breakthrough in the protein structure prediction field using deep learning to predict protein structure from amino acid sequence.
Marx, V. Method of the year: protein structure prediction. Nat. Methods 19, 5–10 (2022).
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).
Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444 (2022).
Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).
Leem, J., de Oliveira, S. H. P., Krawczyk, K. & Deane, C. M. STCRDab: the structural T-cell receptor database. Nucleic Acids Res. 46, D406–D412 (2018). Automated database for curating TCR and TCR–pMHC structures from the PDB.
Gowthaman, R. & Pierce, B. G. TCR3d: the T cell receptor structural repertoire database. Bioinformatics 35, 5323–5325 (2019).
Abanades, B. et al. ImmuneBuilder: deep-learning models for predicting the structures of immune proteins. Commun. Biol. 6, 1–8 (2023). Adaptation of AlphaFold to predict TCR structures.
Yin, R. et al. TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning. Nucleic Acids Res. 51, W569–W576 (2023). Adaptation of AlphaFold to specifically predict TCRs and TCR–pMHC complex structures from sequences.
Davis, M. M. & Bjorkman, P. J. T-cell antigen receptor genes and T-cell recognition. Nature 334, 395–402 (1988).
Bradley, P. Structure-based prediction of T cell receptor:peptide-MHC interactions. eLife 12, e82813 (2023).
Dash, P. et al. Quantifiable predictive features define epitope specific T cell receptor repertoires. Nature 547, 89–93 (2017).
histo.fyi — An Interactive Exploration of the Structure and Function of MHC Molecules (2022); https://www.histo.fyi/
Ponomarenko, J. et al. IEDB-3D: structural data within the immune epitope database. Nucleic Acids Res. 39, D1164–D1170 (2011).
Davis, M. M. et al. Ligand recognition by αβ T cell receptors. Annu. Rev. Immunol. 16, 523–544 (1998).
Merwe der van, P. A. & Davis, S. J. Molecular interactions mediating T cell antigen recognition. Annu. Rev. Immunol. 21, 659–684 (2003).
Cole, D. K. et al. Human TCR-binding affinity is governed by MHC class restriction. J. Immunol. 178, 5727–5734 (2007).
Riley, T. P. & Baker, B. M. The intersection of affinity and specificity in the development and optimization of T cell receptor based therapeutics. Semin. Cell Dev. Biol. 84, 30–41 (2018).
Rossjohn, J. et al. T cell antigen receptor recognition of antigen-presenting molecules. Annu. Rev. Immunol. 33, 169–200 (2015).
Kjer-Nielsen, L. et al. A structural basis for the selection of dominant αβ T cell receptors in antiviral immunity. Immunity 18, 53–64 (2003).
Tynan, F. E. et al. A T cell receptor flattens a bulged antigenic peptide presented by a major histocompatibility complex class I molecule. Nat. Immunol. 8, 268–276 (2007).
Moult, J. A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. Curr. Opin. Struct. Biol. 15, 285–289 (2005).
Laine, E., Eismann, S., Elofsson, A. & Grudinin, S. Protein sequence-to-structure learning: Is this the end(-to-end revolution)? Proteins 89, 1770–1786 (2021).
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).
Muhammed, M. T. & Aki-Yalcin, E. Homology modeling in drug discovery: overview, current applications, and future perspectives. Chem. Biol. Drug Des. 93, 12–20 (2019).
Šali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).
Kuhlman, B. & Bradley, P. Advances in protein structure prediction and design. Nat. Rev. Mol. Cell Biol. 20, 681–697 (2019).
Klausen, M. S., Anderson, M. V., Jespersen, M. C., Nielsen, M. & Marcatili, P. LYRA, a webserver for lymphocyte receptor structural modeling. Nucleic Acids Res. 43, W349–W355 (2015).
Gowthaman, R. & Pierce, B. G. TCRmodel: high resolution modeling of T cell receptors from sequence. Nucleic Acids Res. 46, W396–W401 (2018).
Schritt, D. et al. Repertoire Builder: high-throughput structural modeling of B and T cell receptors. Mol. Syst. Des. Eng. 4, 761–768 (2019).
Wong, W. K. et al. TCRBuilder: multi-state T-cell receptor structure prediction. Bioinformatics 36, 3580–3581 (2020).
Jensen, K. K. et al. TCRpMHCmodels: structural modelling of TCR–pMHC class I complexes. Sci. Rep. 9, 14530 (2019).
Li, S. et al. in In Vitro Differentiation of T-Cells: Methods and Protocols (ed. Kaneko, S.) 207–229 (Springer, 2019).
Larsson, P., Wallner, B., Lindahl, E. & Elofsson, A. Using multiple templates to improve quality of homology models in automated homology modeling. Protein Sci. 17, 990–1002 (2008).
Milighetti, M., Shawe-Taylor, J. & Chain, B. Predicting T cell receptor antigen specificity from structural features derived from homology models of receptor–peptide–major histocompatibility complexes. Front. Physiol. 12, 730908 (2021).
Wong, W. K., Leem, J. & Deane, C. M. Comparative analysis of the CDR loops of antigen receptors. Front. Immunol. 10, 2454 (2019).
Burke, D. F. et al. Towards a structurally resolved human protein interaction network. Nat. Struct. Mol. Biol. 30, 216–225 (2023).
Baek, M. et al. Efficient and accurate prediction of protein structure using RoseTTAFold2. Preprint at bioRxiv https://doi.org/10.1101/2023.05.24.542179 (2023).
Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. Preprint at bioRxiv https://doi.org/10.1101/2021.10.04.463034 (2022). Extension of AlphaFold to support prediction of multimeric protein structures from sequences.
Aronson, A., Hochner, T., Cohen, T. & Schneidman-Duhovny, D. Structure modeling and specificity of peptide–MHC class I interactions using geometric deep learning. Preprint at bioRxiv https://doi.org/10.1101/2022.12.15.520566 (2022).
Marzella, D. F. et al. PANDORA: a fast, anchor-restrained modelling protocol for peptide:MHC complexes. Front. Immunol. 13, 878762 (2022).
Nguyen, A. T., Szeto, C. & Gras, S. The pockets guide to HLA class I molecules. Biochem. Soc. Trans. 49, 2319–2331 (2021).
Koehler Leman, J. et al. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat. Methods 17, 665–680 (2020).
Motmaen, A. et al. Peptide-binding specificity prediction using fine-tuned protein structure prediction networks. Proc. Natl Acad. Sci. USA 120, e2216697120 (2023).
Mikhaylov, V. et al. Accurate modeling of peptide-MHC structures with AlphaFold. Structure 32, 228–241.e4 (2024).
Cohen, T., Halfon, M. & Schneidman-Duhovny, D. NanoNet: rapid and accurate end-to-end nanobody modeling by deep learning. Front. Immunol. 13, 958584 (2022).
Delaunay, A. P. et al. Peptide–MHC structure prediction with mixed residue and atom graph neural network. Preprint at bioRxiv https://doi.org/10.1101/2022.11.23.517618 (2022).
Aithani, L. et al. Advancing structural biology through breakthroughs in AI. Curr. Opin. Struct. Biol. 80, 102601 (2023).
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).
Faruk, N. F., Peng, X., Freed, K. F., Roux, B. & Sosnick, T. R. Challenges and advantages of accounting for backbone flexibility in prediction of protein–protein complexes. J. Chem. Theory Comput. 18, 2016–2032 (2022).
Fernández-Quintero, M. L., Pomarici, N. D., Loeffler, J. R., Seidler, C. A. & Liedl, K. R. T-cell receptor CDR3 loop conformations in solution shift the relative Vα-Vβ domain distributions. Front. Immunol. 11, 1440 (2020).
Rice, M. T. et al. Recognition of the antigen-presenting molecule MR1 by a Vδ3+ γδ T cell receptor. Proc. Natl Acad. Sci. USA 118, e2110288118 (2021).
Singh, N. K. et al. Geometrical characterization of T cell receptor binding modes reveals class-specific binding to maximize access to antigen. Proteins 88, 503–513 (2020).
Google DeepMind AlphaFold Team & Isomorphic Labs Team. Performance and structural coverage of the latest, in-development AlphaFold model. https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/a-glimpse-of-the-next-generation-of-alphafold/alphafold_latest_oct2023.pdf (2023).
Baek, M. et al. Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA. Nat. Methods 21, 117–121 (2024).
Le Nours, J. et al. A class of γδ T cell receptors recognize the underside of the antigen-presenting molecule MR1. Science 366, 1522–1527 (2019).
Nolan, S. et al. A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-51964/v1 (2020).
Corrie, B. D. et al. iReceptor: a platform for querying and analyzing antibody/B-cell and T-cell receptor repertoire data across federated repositories. Immunol. Rev. 284, 24–41 (2018).
Das, S. & Chakrabarti, S. Classification and prediction of protein–protein interaction interface using machine learning algorithm. Sci. Rep. 11, 1761 (2021).
Basu, S. & Wallner, B. DockQ: a quality measure for protein–protein docking models. PLoS ONE 11, e0161879 (2016).
Pierce, B. G. & Weng, Z. A flexible docking approach for prediction of T cell receptor–peptide–MHC complexes. Protein Sci. Publ. Protein Soc. 22, 35–46 (2013).
Pettmann, J. et al. The discriminatory power of the T cell receptor. eLife 10, e67092 (2021).
Peacock, T. & Chain, B. Information-driven docking for TCR–pMHC complex prediction. Front. Immunol. 12, 686127 (2021).
Lanzarotti, E., Marcatili, P. & Nielsen, M. Identification of the cognate peptide–MHC target of T cell receptors using molecular modeling and force field scoring. Mol. Immunol. 94, 91–97 (2018).
Acknowledgements
This work was supported by funding from the UK Medical Research Council grant number MC_UU_12010/3 to H.K., the UK Medical Research Council grant number MC_UU_00008 to B.M., an ARISE Fellowship from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement number 945405 to C.T., the UK Medical Research Council grant number HBR01480 to G.O., the NIHR Oxford Biomedical Research Centre, the Wellcome Trust grant number 209222_Z_17_Z to G.O., the CAMS Innovation Fund for Medical Sciences (CIFMS) grant number 2018-I2M-2-002 to G.O., and the Misses Barrie Charitable Trust. We would like to thank A. Greenshields-Watson, Y.L. Chen, C. Lee and J. Rossjohn for their critical review of the article before submission. B.M. would like to thank D. Hudson, N. Quast, M. Raybould and F. Spoendlin for their insightful conversations. We would also like to thank the developers of PyMol, pandas, NumPy, Matplotlib and seaborn for their contributions to the open-source community. These tools and packages were used to generate the figures and numerical summaries throughout the work.
Author information
Authors and Affiliations
Contributions
B.M. researched and wrote the article. C.T. and G.O. provided the content for some sections and reviewed and edited the article before submission. C.M.D. supervised, reviewed and edited the article. H.K. conceived the content and supervised the writing of the work.
Corresponding author
Ethics declarations
Competing interests
G.O. is the founder of T-Cypher Bio. All other authors declare no competing interests.
Peer review
Peer review information
Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Madhura Mukhopadhyay, in collaboration with the Nature Methods team
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
McMaster, B., Thorpe, C., Ogg, G. et al. Can AlphaFold’s breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02240-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41592-024-02240-7