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Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen

Abstract

T cell receptor (TCR) recognition of foreign peptides presented by major histocompatibility complex protein is a major event in triggering the adaptive immune response to pathogens or cancer. The prediction of TCR–peptide interactions has great importance for therapy of cancer as well as infectious and autoimmune diseases but remains a major challenge, particularly for novel (unseen) peptide epitopes. Here we present TCRen, a structure-based method for ranking candidate unseen epitopes for a given TCR. The first stage of the TCRen pipeline is modeling of the TCR–peptide–major histocompatibility complex structure. Then a TCR–peptide residue contact map is extracted from this structure and used to rank all candidate epitopes on the basis of an interaction score with the target TCR. Scoring is performed using an energy potential derived from the statistics of TCR–peptide contact preferences in existing crystal structures. We show that TCRen has high performance in discriminating cognate versus unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes.

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Fig. 1: Description of the TCRen method.
Fig. 2: The performance of TCRen in distinguishing cognate TCR epitopes from unrelated peptides.
Fig. 3: A comparison of TCRen with structure-based methods for the prediction of general protein interactions.
Fig. 4: The performance of TCRen when homology models are used as input.
Fig. 5: TCRen for the prediction of cancer neoepitopes recognized by TILs.

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Data availability

All crystal structures of TCR–peptide–MHC complexes from the PDB used to derive the TCRen statistical potential and the datasets from previously published studies used to validate the performance of TCRen are available via GitHub at https://github.com/antigenomics/tcren-ms. Data used for benchmarking was taken from previously published studies; references are given in Table 1. Source data are provided with this paper.

Code availability

All the code and data required to reproduce the analysis performed in the study, as well as a script and tutorial for running TCRen on new data, are available via GitHub at https://github.com/antigenomics/tcren-ms. Code for the TCRen pipeline is also available via Zenodo at https://doi.org/10.5281/zenodo.11129800 (ref. 41). All analysis was performed using R version 4.2.0, homology modeling was performed using TCRpMHCmodels version 1.0 and NetMHCIIpan-4.0 software was used to predict peptide binding to MHC class II.

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Acknowledgements

MD simulations were carried out with the use of computational facilities of the Supercomputer Center ‘Polytechnical’ at the St. Petersburg Polytechnic University. We thank S. Bobisse and A. Harari for providing data for 302TIL candidate neoepitopes. The study was supported by a grant from the Ministry of Science and Higher Education of Russian Federation (075-15-2019-1789). MD simulations were supported by the HSE University Basic Research Program.

Author information

Authors and Affiliations

  1. Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia

    Vadim K. Karnaukhov & Dmitriy M. Chudakov

  2. Shemyakin−Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia

    Vadim K. Karnaukhov, Dmitrii S. Shcherbinin, Anton O. Chugunov, Dmitriy M. Chudakov, Roman G. Efremov, Ivan V. Zvyagin & Mikhail Shugay

  3. Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia

    Dmitrii S. Shcherbinin, Dmitriy M. Chudakov, Ivan V. Zvyagin & Mikhail Shugay

  4. Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Russia

    Anton O. Chugunov & Roman G. Efremov

  5. Central European Institute of Technology, Brno, Czech Republic

    Dmitriy M. Chudakov

  6. Higher School of Economics, Moscow, Russia

    Roman G. Efremov

Authors

  1. Vadim K. Karnaukhov
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  2. Dmitrii S. Shcherbinin
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  3. Anton O. Chugunov
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  4. Dmitriy M. Chudakov
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  5. Roman G. Efremov
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  6. Ivan V. Zvyagin
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  7. Mikhail Shugay
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Contributions

Conceptualization was carried out by V.K.K. and M.S. Methodology was planned by V.K.K. and M.S. Validation was performed by V.K.K. Curation of the database of TCR–peptide–MHC structures from the PDB was carried out by D.S.S. Supervison was performed by M.S., I.V.Z. and D.M.C. Supervison of MD simulations was performed by A.O.C. and R.G.E. Writing of the original draft was done by V.K.K. Writing, review and editing was done by V.K.K., M.S., I.V.Z., D.M.C., A.O.C. and R.G.E.

Corresponding authors

Correspondence to
Vadim K. Karnaukhov, Dmitriy M. Chudakov or Mikhail Shugay.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Computational Science thanks Shuai Cheng and Andrew Fiore-Gartland for their contributions to the peer review of this work. Primary Handling Editor: Ananya Rastogi, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–16, Tables 1–2 and Notes 1–2.

Reporting Summary

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Supplementary Data 1.

The nonredundant set of crystal structures of TCR–peptide–MHC complexes from the PDB that was used to derive the TCRen potential.

Source data

Source Data Fig. 2.

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

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Karnaukhov, V.K., Shcherbinin, D.S., Chugunov, A.O. et al. Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen.
Nat Comput Sci (2024). https://doi.org/10.1038/s43588-024-00653-0

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  • Received: 16 July 2023

  • Accepted: 04 June 2024

  • Published: 10 July 2024

  • DOI: https://doi.org/10.1038/s43588-024-00653-0

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