TCR-H: Machine Learning Prediction of T-cell Receptor Epitope Binding on Unseen Datasets

Abstract

AI/ML approaches to predicting T-cell receptor (TCR) epitope specificity achieve high performance metrics on test datasets which include sequences that are also part of the training set but fail to generalize to test sets consisting of epitopes and TCRs that are absent from the training set, i.e., unseen. We present TCR-H, a supervised classification Support Vector Machines model using physicochemical features trained on the largest dataset available to date using only experimentally validated non-binders as negative datapoints. TCR-H exhibits an area under the curve of the receiver-operator characteristic (AUC of ROC) of 0.87 for epitope hard splitting (i.e., on test sets with all epitopes unseen), 0.92 for TCR hard splitting and 0.89 for strict splitting in which neither the epitopes nor the TCRs in the test set are seen in the training data. TCR-H may thus represent a significant step towards general applicability of epitope:TCR specificity prediction.