Computational Methods for Predicting Key Interactions in T Cell-Mediated Adaptive Immunity.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Ryan Ehrlich, Eric Glynn, Mona Singh, Dario Ghersi
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引用次数: 0

Abstract

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.

预测 T 细胞介导的适应性免疫中关键相互作用的计算方法。
适应性免疫系统能够识别病原体和癌症的特异性特征,并具有记忆能力,使其能够快速有效地应对与相同抗原的反复接触。T 细胞在适应性免疫系统中发挥着核心作用,它直接针对细胞内病原体,并帮助激活 B 细胞分泌抗体。有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的相互作用,以及T细胞受体与肽-MHC复合体之间的相互作用--是T细胞能够精确识别抗原的基础。预测这些相互作用的计算方法正越来越多地应用于医学相关领域,包括疫苗设计和预测患者对癌症免疫疗法的反应。我们为计算研究人员提供了关于适应性免疫系统的通俗易懂的介绍,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,并重点介绍了仍然存在的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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