T-cell receptor alpha to beta chains binding prediction

Abstract Binding of T-cell receptors (TCRs) and their cognate peptide-major histocompatibility complex (pMHC) target is determined by both TCR and TCR chains. However, not all TCR and TCR can bind to each other. Predicting their pairing is crucial for understanding the TCR–pMHC interaction and developing effective de novo TCRs. Here, we show that in the general TCR repertoire, TCR and TCR chain compositions are independent. However, in pMHC-binding TCRs, clear associations between TCR and TCR chains are found, also for TCRs binding to the same pMHC. The association between the CDR3 amino acid composition and V, J usage of TCR and TCR reveals distinct binding patterns between specific V and J genes, as well as negative correlations between the charge and polarity of the TCR and TCR chains, but positive associations between their molecular weights. These associations are used for the development of a prediction model for TCR and TCR pairing. We present here TCR-BARN (TCR Beta-Alpha chains paiRing using Nlp) that employs an initial embedding for each amino acid in the TCR alpha and beta CDR3 sequences, followed by long short-term memory (LSTM) networks to capture sequence dependencies. The V and J genes are represented using one-hot encoding. LSTM outputs are concatenated and passed through a fully connected feedforward layer for binding prediction. TCR–BARN reaches an area under the curve 0. 65 0. 007 for epitope-bound TCRs. TCR–BARN can be used for generating cognate TCRs resembling natural TCRs and evaluating the generated TCR quality.