T cell receptor cross-reactivity prediction improved by a comprehensive mutational scan database
Amitava Banerjee1, David J Pattinson2, Cornelia L Wincek3
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
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.
Insights
Mapping T cell receptor (TCR) targets is crucial for TCR therapies. We developed the BATCAVE database and BATMAN model to accurately predict TCR-peptide interactions and efficiently map TCR targets, improving prediction accuracy for mutant antigens.
Area of Science:
- Immunology
- Computational Biology
- Bioinformatics
Background:
- Mapping T cell receptor (TCR) targets is essential for predicting pathogenic escape and off-target effects in TCR therapies.
- Current challenges include a lack of unbiased benchmarking datasets and sensitive computational methods for small-peptide mutations.
Purpose of the Study:
- To create a comprehensive database and develop a computational model for accurate TCR target mapping.
- To enable efficient identification of TCR targets for novel TCRs.
Main Methods:
- Curated the Benchmark for Activation of T cells with Cross-reactive Avidity for Epitopes (BATCAVE) database, containing over 22,000 TCR-peptide pairs from single-amino-acid mutational assays.
- Developed Bayesian Inference of Activation of TCR by Mutant Antigens (BATMAN), an interpretable Bayesian model trained on BATCAVE.
- Implemented an active learning extension for efficient target mapping of novel TCRs.
Main Results:
- BATMAN outperforms existing methods in predicting TCR-peptide interactions.
- The model reveals key structural and biochemical predictors of TCR-peptide binding.
- BATMAN accurately predicts polyclonal T cell responses and identifies TCR targets with high sequence dissimilarity.
Conclusions:
- The BATCAVE database and BATMAN model provide a robust framework for TCR target mapping.
- This approach enhances the prediction of TCR-peptide interactions and facilitates the development of safer and more effective TCR therapies.
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