School of Interdisciplinary Informatics, University of Nebraska, Omaha, Nebraska, USA; email:
Lewis-Sigler Institute, Princeton University, Princeton, New Jersey, USA.
Annu Rev Biomed Data Sci. 2024 Aug;7(1):295-316. doi: 10.1146/annurev-biodatasci-102423-122741. Epub 2024 Jul 24.
The adaptive immune system recognizes pathogen- and cancer-specific features and is endowed with memory, enabling it to respond quickly and efficiently to repeated encounters with the same antigens. T cells play a central role in the adaptive immune system by directly targeting intracellular pathogens and helping to activate B cells to secrete antibodies. Several fundamental protein interactions-including those between major histocompatibility complex (MHC) proteins and antigen-derived peptides as well as between T cell receptors and peptide-MHC complexes-underlie the ability of T cells to recognize antigens with great precision. Computational approaches to predict these interactions are increasingly being used for medically relevant applications, including vaccine design and prediction of patient response to cancer immunotherapies. We provide computational researchers with an accessible introduction to the adaptive immune system, review computational approaches to predict the key protein interactions underlying T cell-mediated adaptive immunity, and highlight remaining challenges.
适应性免疫系统识别病原体和肿瘤特异性特征,并具有记忆功能,使其能够快速有效地对同一抗原的重复接触做出反应。T 细胞通过直接靶向细胞内病原体并帮助激活 B 细胞分泌抗体,在适应性免疫系统中发挥核心作用。几种基本的蛋白质相互作用——包括主要组织相容性复合体 (MHC) 蛋白与抗原衍生肽之间以及 T 细胞受体与肽-MHC 复合物之间的相互作用——是 T 细胞能够非常精确地识别抗原的基础。预测这些相互作用的计算方法越来越多地用于具有医学相关性的应用,包括疫苗设计和预测癌症免疫疗法对患者的反应。我们为计算研究人员提供了对适应性免疫系统的易访问介绍,回顾了预测 T 细胞介导的适应性免疫的关键蛋白质相互作用的计算方法,并强调了仍然存在的挑战。