TCRMatch: Predicting T-Cell Receptor Specificity Based on Sequence Similarity to Previously Characterized Receptors

Autor: William D. Chronister, Austin Crinklaw, Swapnil Mahajan, Randi Vita, Zeynep Koşaloğlu-Yalçın, Zhen Yan, Jason A. Greenbaum, Leon E. Jessen, Morten Nielsen, Scott Christley, Lindsay G. Cowell, Alessandro Sette, Bjoern Peters
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Frontiers in Immunology, Vol 12 (2021)
Druh dokumentu: article
ISSN: 1664-3224
DOI: 10.3389/fimmu.2021.640725
Popis: The adaptive immune system in vertebrates has evolved to recognize non-self antigens, such as proteins expressed by infectious agents and mutated cancer cells. T cells play an important role in antigen recognition by expressing a diverse repertoire of antigen-specific receptors, which bind epitopes to mount targeted immune responses. Recent advances in high-throughput sequencing have enabled the routine generation of T-cell receptor (TCR) repertoire data. Identifying the specific epitopes targeted by different TCRs in these data would be valuable. To accomplish that, we took advantage of the ever-increasing number of TCRs with known epitope specificity curated in the Immune Epitope Database (IEDB) since 2004. We compared seven metrics of sequence similarity to determine their power to predict if two TCRs have the same epitope specificity. We found that a comprehensive k-mer matching approach produced the best results, which we have implemented into TCRMatch, an openly accessible tool (http://tools.iedb.org/tcrmatch/) that takes TCR β-chain CDR3 sequences as an input, identifies TCRs with a match in the IEDB, and reports the specificity of each match. We anticipate that this tool will provide new insights into T cell responses captured in receptor repertoire and single cell sequencing experiments and will facilitate the development of new strategies for monitoring and treatment of infectious, allergic, and autoimmune diseases, as well as cancer.
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