Abstract
IntroductionPredicting the binding specificity of T Cell Receptors (TCR) to MHC-peptide complexes (pMHCs) is essential for the development of repertoire-based biomarkers. This affinity may be affected by different components of the TCR, the peptide, and the MHC allele. Historically, the main element used in TCR-peptide binding prediction was the Complementarity Determining Region 3 (CDR3) of the beta chain. However, recently the contribution of other components, such as the alpha chain and the other V gene CDRs has been suggested. We use a highly accurate novel deep learning-based TCR-peptide binding predictor to assess the contribution of each component to the binding.MethodsWe have previously developed ERGO-I (pEptide tcR matchinG predictiOn), a sequence-based T-cell receptor (TCR)-peptide binding predictor that employs natural language processing (NLP) -based methods. We improved it to create ERGO-II by adding the CDR3 alpha segment, the MHC typing, V and J genes, and T cell type (CD4+ or CD8+) as to the predictor. We then estimate the contribution of each component to the prediction.Results and DiscussionERGO-II provides for the first time high accuracy prediction of TCR-peptide for previously unseen peptides. For most tested peptides and all measures of binding prediction accuracy, the main contribution was from the beta chain CDR3 sequence, followed by the beta chain V and J and the alpha chain, in that order. The MHC allele was the least contributing component. ERGO-II is accessible as a webserver at http://tcr2.cs.biu.ac.il/ and as a standalone code at https://github.com/IdoSpringer/ERGO-II.
Highlights
Predicting the binding specificity of T Cell Receptors (TCR) to major histocompatibility complexes (MHCs)-peptide complexes is essential for the development of repertoire-based biomarkers
A high accuracy TCR-peptide binding predictor discerns the relative impact of T Cell Receptor alpha and beta Complementarity Determining Region 3 (CDR3), MHC, V and J genes to peptide binding prediction
ERGO-II was created by adding the CDR3 alpha segment, the MHC typing, V and J genes, and T cell type (CD4+ or CD8+) to the ERGO-I predictor
Summary
Predicting the binding specificity of T Cell Receptors (TCR) to MHC-peptide complexes (pMHCs) is essential for the development of repertoire-based biomarkers. The contribution of the different components of the T-cell receptor and the peptide- bound MHC (pMHC) to the binding has never been fully resolved Estimating this contribution is important for the prediction of peptide-TCR binding and the design of novel TCRs. We have previously developed ERGO-I (pEptide tcR matchinG predictiOn), a highly specific and generic sequence-based TCR-peptide binding predictor based on novel deep learning methods which utilizes parallel embeddings of TCR and peptides in a joint neural network [5]. We hypothesized that the difference resulted from the varying relative contribution of the beta chain CDR3 sequence to the TCR-peptide binding prediction accuracy. We developed ERGO-II characterized by extended embedding that contain other components and tested their contribution to the TCR-pMHC binding prediction accuracy
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