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$\alpha $ and TCR$\beta $ chains. However, not all TCR$\alpha $ and TCR$\beta $ 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$\alpha $ and TCR$\beta $ chain compositions are independent. However, in pMHC-binding TCRs, clear associations between TCR$\alpha $ and TCR$\beta $ 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$\alpha $ and TCR$\beta $ reveals distinct binding patterns between specific $V$ and $J$ genes, as well as negative correlations between the charge and polarity of the TCR$\alpha $ and TCR$\beta $ chains, but positive associations between their molecular weights. These associations are used for the development of a prediction model for TCR$\alpha $ and TCR$\beta $ 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 $>0.65\pm 0.007$ for epitope-bound TCRs. TCR-BARN can be used for generating cognate TCRs resembling natural TCRs and evaluating the generated TCR quality.