BATMAN: Improved T cell receptor cross-reactivity prediction benchmarked on a comprehensive mutational scan database
Banerjee, A.; Pattinson, D. J.; Wincek, C. L.; Bunk, P.; Chapin, S. R.; Navlakha, S.; Meyer, H. V.
Show abstract
Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database, called BATCAVE, encompassing complete single amino acid mutational assays of more than 22,000 TCR-peptide pairs, centered around 25 immunogenic human and mouse epitopes, across both major histocompatibility complex classes, against 151 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. We also developed an active learning version of BATMAN, which can efficiently learn the binding profile of a novel TCR by selecting an informative yet small number of peptides to assay. When validated on our database, BATMAN outperforms existing methods and reveals important biochemical predictors of TCR-peptide interactions. Finally, we demonstrate the broad applicability of BATMAN, including for predicting off-target effects for TCR-based therapies and polyclonal T cell responses.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- APMAT analysis reveals the association between CD8 T cell receptors, cognate antigen, and T cell phenotype and persistence 96%
- NeoPrecis: Enhancing Immunotherapy Response Prediction through Integration of Qualified Immunogenicity and Clonality-Aware Neoantigen Landscapes 96%
- Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells 96%
Similar papers in this journal
- Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes 95%
- Scalable TCR synthesis and screening enables antigen reactivity mapping in vitiligo 94%
- IL-9 as a naturally orthogonal cytokine with optimal JAK/STAT signaling for engineered T cell therapy 93%
Similar papers in this journal
- Reproducible single cell annotation of programs underlying T-cell subsets, activation states, and functions 95%
- Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions 95%
- TIRTL-seq: Deep, quantitative, and affordable paired TCR repertoire sequencing 94%
Similar papers in this journal
- Deep generative selection models of T and B cell receptor repertoires with soNNia 96%
- CryoEM structure of an MHC-I/TAPBPR peptide bound intermediate reveals the mechanism of antigen proofreading 95%
- Phenotypic determinism and stochasticity in antibody repertoires of clonally expanded plasma cells 95%
"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.