Recent advances in molecular and bioinformatic methods have greatly improved our ability to study the formation of an adaptive immune response towards foreign pathogens, self-antigens, and cancer neoantigens. T cell receptors (TCR) are the key players in this process that recognize peptides presented by major histocompatibility complex (MHC). Owing to the huge diversity of both TCR sequence variants and peptides they recognize, accumulation and complex analysis of large amounts of TCR-antigen specificity data is required for understanding the structure and features of adaptive immune responses towards pathogens, vaccines, cancer, as well as autoimmune responses. In the present review, we summarize recent efforts on gathering and interpreting TCR-antigen specificity data and outline the critical role of tighter integration with other immunoinformatics data sources that include epitope MHC restriction, TCR repertoire structure models, and TCR/peptide/MHC structural data. We suggest that such integration can lead to the ability to accurately annotate individual TCR repertoires, efficiently estimate epitope and neoantigen immunogenicity, and ultimately, in silico identify TCRs specific to yet unstudied antigens and predict self-peptides related to autoimmunity.

中文翻译：

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免疫信息学方法和数据库的概述，将T细胞受体库与它们的抗原特异性联系起来。

分子和生物信息学方法的最新进展极大地提高了我们研究针对外来病原体，自身抗原和癌症新抗原的适应性免疫应答形成的能力。T细胞受体（TCR）是识别主要组织相容性复合物（MHC）呈递的肽的主要过程。由于TCR序列变体和肽的多样性，它们需要识别，积累和复杂的大量TCR抗原特异性数据，才能理解针对病原体，疫苗，癌症以及其他疾病的适应性免疫反应的结构和特征。自身免疫反应。在目前的评论中，我们总结了最近在收集和解释TCR抗原特异性数据方面所做的努力，并概述了与其他免疫信息学数据源（包括表位MHC限制，TCR组成结构模型和TCR /肽/ MHC结构数据）更紧密整合的关键作用。我们建议这种整合可以导致准确注释单个TCR组成成分，有效估计表位和新抗原免疫原性的能力，并最终在计算机上鉴定对尚未研究的抗原具有特异性的TCR，并预测与自身免疫相关的自身肽。