2023 article

RACER-m Leverages Structural Features for Sparse T Cell Specificity Prediction

Wang, A., Lin, X., Chau, K. N., Onuchic, J. N., Levine, H., & George, J. T. (2023, August 7). (Vol. 8). Vol. 8.

By: A. Wang *, X. Lin n , K. Chau *, J. Onuchic *, H. Levine * & J. George *

TL;DR: RACER-m is presented, a coarse-grained structural template model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures and capably identifies biophysically meaningful point-mutants that affect overall binding affinity, distinguishing its ability in predicting TCR specificity of point mutants peptides from alternative sequence-based methods. (via Semantic Scholar)
Source: ORCID
Added: February 1, 2024

AbstractReliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated primarily 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 structural interface to accurately predict the MHC-allele-restricted TCR-peptide binding interactions remained challenging. Here, we present RACER-m, a coarse-grained structural template model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. We find explicit inclusion of structural content substantially reduces the required number of training examples for reliable prediction of TCR-recognition specificity and sensitivity across diverse biological contexts. We demonstrate that our structural model capably identifies biophysically meaningful point-mutants that affect overall binding affinity, distinguishing its ability in predicting TCR specificity of point mutants peptides from alternative sequence-based methods. Collectively, our approach combines biophysical and inferential learning-based methods to predict TCR-peptide binding events using sparse training data. Its application is broadly applicable to studies involving both closely-related and structurally diverse TCR-peptide pairs.