Banerjee, Amitava, Pattinson, David J, Wincek, Cornelia L, Bunk, Paul, Axhemi, Armend, Chapin, Sarah R, Navlakha, Saket, Meyer, Hannah V (July 2025) T cell receptor cross-reactivity prediction improved by a comprehensive mutational scan database. Cell Systems. p. 101345. ISSN 2405-4720 (Public Dataset)
Abstract
Comprehensively mapping all targets of a T cell receptor (TCR) is important for predicting pathogenic escape and off-target effects of TCR therapies. However, this mapping has been challenging due to lack of unbiased benchmarking datasets and computational methods sensitive to small-peptide mutations. To address this, we curated the benchmark for activation of T cells with cross-reactive avidity for epitopes (BATCAVE) database, encompassing near-complete single-amino-acid mutational assays, centered around 25 immunogenic epitopes, across both major histocompatibility complex classes, against 151 human and mouse TCRs, containing 22,000+ TCR-peptide pairs in total. We then introduce Bayesian inference of activation of TCR by mutant antigens (BATMAN), an interpretable Bayesian model, trained on BATCAVE, for predicting the peptides that activate a TCR, and an active learning extension, which efficiently maps targets of a novel TCR by selecting a few peptides to assay. We show that BATMAN outperforms existing methods, reveals structural and biochemical predictors of TCR-peptide interactions, and can predict polyclonal T cell responses and TCR targets with high sequence dissimilarity. A record of this paper's transparent peer review process is included in the supplemental information.
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