Predicting recognition between T cell receptors and epitopes with TCRGP

Adaptive immune system uses T cell receptors (TCRs) to recognize pathogens and to consequently initiate immune responses. TCRs can be sequenced from individuals and methods analyzing the specificity of the TCRs can help us better understand individuals' immune status in different disorders. For this task, we have developed TCRGP, a novel Gaussian process method that predicts if TCRs recognize specified epitopes. TCRGP can utilize the amino acid sequences of the complementarity determining regions (CDRs) from TCR alpha and TCR beta chains and learn which CDRs are important in recognizing different epitopes. Our comprehensive evaluation with epitope-specific TCR sequencing data shows that TCRGP achieves on average higher prediction accuracy in terms of AUROC score than existing state-of-the-art methods in epitope-specificity predictions. We also propose a novel analysis approach for combined single-cell RNA and TCR alpha beta (scRNA+TCR alpha beta) sequencing data by quantifying epitope-specific TCRs with TCRGP and identify HBV-epitope specific T cells and their transcriptomic states in hepatocellular carcinoma patients. Author summary Humans have dedicated cells called T cells that recognize potentially harmful invaders using T cell receptors (TCRs) and protect us from infections. Each person has billions of T cells and millions of different kinds of TCRs that enable us to recognize a large variety of invaders. When for example a virus enters the body, T cells with TCRs recognizing that virus multiply and fight it. After such attacks, T cells that recognized the virus, remain in the body, forming an immunological memory. T cells thus characterize a person's infection history, but in order to reveal it, we need to determine which epitopes, molecular signatures of the invaders, different TCRs can recognize. We can characterize which TCRs recognize certain epitopes experimentally, but this is a time and sample consuming task and is not often possible with scarce patient samples such as biopsies. However, previously produced experimental data has enabled us to develop a computational method, TCRGP, that can predict which epitopes a TCR recognizes with a higher accuracy than previous methods. As a computational method, TCRGP spares resources from time and sample consuming experimental validations and offers an interesting way to analyze publicly available TCR data.

Automated summary

TCRGP is a novel computational method that predicts TCR recognition of specific epitopes with higher accuracy than existing methods. By quantifying epitope-specific TCRs and identifying HBV-epitope specific T cells in hepatocellular carcinoma patients, TCRGP offers a valuable tool for analyzing publicly available TCR data.