Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs

Abstract

One Sentence Summary: The combination of advanced tools from natural language processing and large-scale dictionaries of T cell receptors and their target peptide precisely predicts whether a T cell would bind a specific target. AbstractThe T cell repertoire is composed of T cell receptors (TCR) selected by their cognate MHCpeptides and naive TCR that do not bind known peptides. While the task of distinguishing a peptide-binding TCR from a naive TCR unlikely to bind any peptide can be performed using sequence motifs, distinguishing between TCRs binding different peptides requires more advanced methods. Such a prediction is the key for using TCR repertoires as disease-specific biomarkers. We here used large scale TCR-peptide dictionaries with state-of-the-art natural language processing (NLP) methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific classifier to predict which TCR binds to which peptide. We successfully employed ERGO for two related tasks: discrimination between peptide binding and naive TCRs and the more complicated task of distinguishing between TCRs that bind different peptides. We show that ERGO significantly outperforms all current methods for classification of TCRs that bind peptides, but more importantly can distinguish the specific target of a TCR among a large set of peptides. The software implementation and data sets are available at: https://github.com/IdoSpringer/ERGO

Population variability in the generation and thymic selection of T-cell repertoires

“…In the last couple of decades experimental methods have been developed for identifying TCRs specific to given antigens [26][27][28][29]. Based on accumulated TCR binding data [19], computational methods have been proposed recently that can find clusters of similarly reactive TCRs [25,[28][29][30], or to predict TCR specificity to a given epitope using machinelearning techniques [31][32][33][34]. SONIA could be used to learn flexible models of these antigen-specific TCR subsets and to study their organization.…”

Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors

“…First, a key challenge in developing machine learning and statistical models to predict immunogenicity is the lack of true negative datasets for TCR-epitope interaction as well as crossreactivity information. Several groups tackled this limitation by simulating a background or negative data (93,96,97,164). Jurtz et al approached the problem by creating incorrect combinations of TCRs and peptides i.e., linking TCR sequences with a random peptide different from the cognate target, and produced a balanced set of positive and negative data.…”

Main Strategies for the Identification of Neoantigens

“…Such reasoning allows us to suggest that it is possible to perform a simulation based on sequences of peptides and TCR repertoires. Several approaches to predicting epitope-TCR binding were developed (e.g., TCRex [ 126 ], NetTCR [ 127 ], Repitope [ 128 ], ERGO [ 129 ], Deepwalk approach [ 130 ]). For instance, TCRex is based on the principle that similar TCR sequences often target the same epitope [ 126 ], Repitope is based on the idea that sequences of epitopes contain some intrinsic hidden pattern that is prone to activating T cell response [ 128 ].…”