Quantitative approaches for decoding the specificity of the human T cell repertoire

Frontiers in Immunology

Immunology

Zahra S. Ghoreyshi¹  Jason T. George² 

[1] Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States;Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States;Engineering Medicine Program, Texas A&M University, Houston, TX, United States;Center for Theoretical Biological Physics, Rice University, Houston, TX, United States;

关键词: TCR;    pMHC;    binding prediction;    protein-protein interaction;    machine learning;    deep learning;   

DOI  :  10.3389/fimmu.2023.1228873

 received in 2023-05-25, accepted in 2023-08-17,  发布年份 2023

来源: Frontiers

【 摘 要 】

T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method’s mathematical approach, predictive performance, and limitations.

【 授权许可】

Unknown   
Copyright © 2023 Ghoreyshi and George