Wang, AilunLin, XingchengChau, Kevin NgOnuchic, José N.Levine, HerbertGeorge, Jason T.2024-09-102024-09-102024Wang, A., Lin, X., Chau, K. N., Onuchic, J. N., Levine, H., & George, J. T. (2024). RACER-m leverages structural features for sparse T cell specificity prediction. Science Advances, 10(20), eadl0161. https://doi.org/10.1126/sciadv.adl0161https://hdl.handle.net/1911/117870Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.engExcept where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial (CC BY-NC) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.Journal articlesciadv-adl0161https://doi.org/10.1126/sciadv.adl0161