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Predicting TCR-peptide recognition based on residue-level pairwise statistical potential

Karnaukhov, V. K.; Shcherbinin, D. S.; Chugunov, A. O.; Chudakov, D. M.; Efremov, R. G.; Zvyagin, I. V.; Shugay, M.

2022-02-19 immunology
10.1101/2022.02.15.480516 bioRxiv
Show abstract

Prediction of TCR-peptide interactions has great importance for therapy of cancer, infectious and autoimmune diseases, but remains a major challenge, particularly for unseen epitopes. We present a structure-based method that enables scoring of TCR-peptide interactions using an energy potential (TCRen) derived from statistics of TCR-peptide contacts in existing crystal structures. We show that TCRen has high performance in discriminating cognate/unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.