Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Can AlphaFold’s breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?

Nature Methods (2024)Cite this article

Subjects

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

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Interaction between a TCR and pMHC.
Fig. 2: Number of TCR crystal structures added to the PDB over the years, based on entries in the STCRDab38.
Fig. 3: Breakdown of TCR structures available from STCRDab38.

Similar content being viewed by others

References

  1. Vignali, D. A. A., Collison, L. W. & Workman, C. J. How regulatory T cells work. Nat. Rev. Immunol. 8, 523–532 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. 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).

    Article  CAS  PubMed  Google Scholar 

  3. 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).

    Article  CAS  PubMed  Google Scholar 

  4. Robinson, J. et al. The IMGT/HLA database. Nucleic Acids Res. 41, D1222–D1227 (2013).

    Article  CAS  PubMed  Google Scholar 

  5. Bjorkman, P. J. et al. Structure of the human class I histocompatibility antigen, HLA-A2. Nature 329, 506–512 (1987).

    Article  CAS  PubMed  Google Scholar 

  6. 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.

    Article  CAS  PubMed  Google Scholar 

  7. Blum, J. S., Wearsch, P. A. & Cresswell, P. Pathways of antigen processing. Annu. Rev. Immunol. 31, 443–473 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Karamooz, E., Harriff, M. J. & Lewinsohn, D. M. MR1-dependent antigen presentation. Semin. Cell Dev. Biol. 84, 58–64 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zajonc, D. M. The CD1 family: serving lipid antigens to T cells since the Mesozoic era. Immunogenetics 68, 561–576 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Mason, D. A very high level of crossreactivity is an essential feature of the T-cell receptor. Immunol. Today 19, 395–404 (1998).

    Article  CAS  PubMed  Google Scholar 

  12. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 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).

    Article  Google Scholar 

  14. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 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.

  16. Grazioli, F. et al. On TCR binding predictors failing to generalize to unseen peptides. Front. Immunol. 13, 1014256 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 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).

  18. 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).

  19. 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).

    Article  CAS  PubMed  Google Scholar 

  20. Beringer, D. X. et al. T cell receptor reversed polarity recognition of a self-antigen major histocompatibility complex. Nat. Immunol. 16, 1153–1161 (2015).

    Article  CAS  PubMed  Google Scholar 

  21. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Deseke, M. & Prinz, I. Ligand recognition by the γδ TCR and discrimination between homeostasis and stress conditions. Cell. Mol. Immunol. 17, 914–924 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wegrecki, M. et al. Atypical sideways recognition of CD1a by autoreactive γδ T cell receptors. Nat. Commun. 13, 3872 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zareie, P. et al. Canonical T cell receptor docking on peptide–MHC is essential for T cell signaling. Science 372, eabe9124 (2021).

    Article  CAS  PubMed  Google Scholar 

  25. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 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).

  28. Sušac, L. et al. Structure of a fully assembled tumor-specific T cell receptor ligated by pMHC. Cell 185, 3201–3213(2022).

    Article  PubMed  PubMed Central  Google Scholar 

  29. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Hellman, L. M. et al. Improving T cell receptor on-target specificity via structure-guided design. Mol. Ther. 27, 300–313 (2019).

    Article  CAS  PubMed  Google Scholar 

  32. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Marx, V. Method of the year: protein structure prediction. Nat. Methods 19, 5–10 (2022).

    Article  CAS  PubMed  Google Scholar 

  34. Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).

    Article  CAS  PubMed  Google Scholar 

  36. 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).

    Article  CAS  PubMed  Google Scholar 

  37. Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 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.

    Article  CAS  PubMed  Google Scholar 

  39. Gowthaman, R. & Pierce, B. G. TCR3d: the T cell receptor structural repertoire database. Bioinformatics 35, 5323–5325 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 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.

    Article  Google Scholar 

  41. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Davis, M. M. & Bjorkman, P. J. T-cell antigen receptor genes and T-cell recognition. Nature 334, 395–402 (1988).

    Article  CAS  PubMed  Google Scholar 

  43. Bradley, P. Structure-based prediction of T cell receptor:peptide-MHC interactions. eLife 12, e82813 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Dash, P. et al. Quantifiable predictive features define epitope specific T cell receptor repertoires. Nature 547, 89–93 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. histo.fyi — An Interactive Exploration of the Structure and Function of MHC Molecules (2022); https://www.histo.fyi/

  46. Ponomarenko, J. et al. IEDB-3D: structural data within the immune epitope database. Nucleic Acids Res. 39, D1164–D1170 (2011).

    Article  CAS  PubMed  Google Scholar 

  47. Davis, M. M. et al. Ligand recognition by αβ T cell receptors. Annu. Rev. Immunol. 16, 523–544 (1998).

    Article  CAS  PubMed  Google Scholar 

  48. Merwe der van, P. A. & Davis, S. J. Molecular interactions mediating T cell antigen recognition. Annu. Rev. Immunol. 21, 659–684 (2003).

    Article  Google Scholar 

  49. Cole, D. K. et al. Human TCR-binding affinity is governed by MHC class restriction. J. Immunol. 178, 5727–5734 (2007).

    Article  CAS  PubMed  Google Scholar 

  50. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Rossjohn, J. et al. T cell antigen receptor recognition of antigen-presenting molecules. Annu. Rev. Immunol. 33, 169–200 (2015).

    Article  CAS  PubMed  Google Scholar 

  52. 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).

    Article  CAS  PubMed  Google Scholar 

  53. 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).

    Article  CAS  PubMed  Google Scholar 

  54. Moult, J. A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. Curr. Opin. Struct. Biol. 15, 285–289 (2005).

    Article  CAS  PubMed  Google Scholar 

  55. 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).

    Article  CAS  PubMed  Google Scholar 

  56. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 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).

    Article  CAS  PubMed  Google Scholar 

  58. Šali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).

    Article  PubMed  Google Scholar 

  59. Kuhlman, B. & Bradley, P. Advances in protein structure prediction and design. Nat. Rev. Mol. Cell Biol. 20, 681–697 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Gowthaman, R. & Pierce, B. G. TCRmodel: high resolution modeling of T cell receptors from sequence. Nucleic Acids Res. 46, W396–W401 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Schritt, D. et al. Repertoire Builder: high-throughput structural modeling of B and T cell receptors. Mol. Syst. Des. Eng. 4, 761–768 (2019).

    Article  CAS  Google Scholar 

  63. Wong, W. K. et al. TCRBuilder: multi-state T-cell receptor structure prediction. Bioinformatics 36, 3580–3581 (2020).

    Article  CAS  PubMed  Google Scholar 

  64. Jensen, K. K. et al. TCRpMHCmodels: structural modelling of TCR–pMHC class I complexes. Sci. Rep. 9, 14530 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Li, S. et al. in In Vitro Differentiation of T-Cells: Methods and Protocols (ed. Kaneko, S.) 207–229 (Springer, 2019).

  66. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Wong, W. K., Leem, J. & Deane, C. M. Comparative analysis of the CDR loops of antigen receptors. Front. Immunol. 10, 2454 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Burke, D. F. et al. Towards a structurally resolved human protein interaction network. Nat. Struct. Mol. Biol. 30, 216–225 (2023).

  70. 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).

  71. 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.

  72. 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).

  73. Marzella, D. F. et al. PANDORA: a fast, anchor-restrained modelling protocol for peptide:MHC complexes. Front. Immunol. 13, 878762 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Nguyen, A. T., Szeto, C. & Gras, S. The pockets guide to HLA class I molecules. Biochem. Soc. Trans. 49, 2319–2331 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Koehler Leman, J. et al. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat. Methods 17, 665–680 (2020).

    Article  PubMed Central  Google Scholar 

  76. Motmaen, A. et al. Peptide-binding specificity prediction using fine-tuned protein structure prediction networks. Proc. Natl Acad. Sci. USA 120, e2216697120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Mikhaylov, V. et al. Accurate modeling of peptide-MHC structures with AlphaFold. Structure 32, 228–241.e4 (2024).

  78. Cohen, T., Halfon, M. & Schneidman-Duhovny, D. NanoNet: rapid and accurate end-to-end nanobody modeling by deep learning. Front. Immunol. 13, 958584 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. 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).

  80. Aithani, L. et al. Advancing structural biology through breakthroughs in AI. Curr. Opin. Struct. Biol. 80, 102601 (2023).

    Article  CAS  PubMed  Google Scholar 

  81. 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).

    Article  CAS  PubMed  Google Scholar 

  82. 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).

    Article  CAS  PubMed  Google Scholar 

  83. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  84. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. 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).

    Article  CAS  PubMed  Google Scholar 

  86. 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).

  87. Baek, M. et al. Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA. Nat. Methods 21, 117–121 (2024).

  88. 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).

    Article  PubMed  Google Scholar 

  89. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  90. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Das, S. & Chakrabarti, S. Classification and prediction of protein–protein interaction interface using machine learning algorithm. Sci. Rep. 11, 1761 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Basu, S. & Wallner, B. DockQ: a quality measure for protein–protein docking models. PLoS ONE 11, e0161879 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  93. 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).

    Article  CAS  Google Scholar 

  94. Pettmann, J. et al. The discriminatory power of the T cell receptor. eLife 10, e67092 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Peacock, T. & Chain, B. Information-driven docking for TCR–pMHC complex prediction. Front. Immunol. 12, 686127 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. 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).

    Article  CAS  PubMed  Google Scholar 

Download references

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

  1. MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK

    Benjamin McMaster, Graham Ogg & Hashem Koohy

  2. Department of Statistics, University of Oxford, Oxford, UK

    Benjamin McMaster & Charlotte M. Deane

  3. Open Targets, Wellcome Genome Campus, Hinxton, UK

    Christopher Thorpe

  4. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK

    Christopher Thorpe

  5. Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK

    Graham Ogg

  6. Alan Turning Fellow in Health and Medicine, University of Oxford, Oxford, UK

    Hashem Koohy

Authors
  1. Benjamin McMaster
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christopher Thorpe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Graham Ogg
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Charlotte M. Deane
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hashem Koohy
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Correspondence to Hashem Koohy.

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41592-024-02240-7

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing