免疫信息学:预测多肽- mhc结合。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Morten Nielsen, Massimo Andreatta, Bjoern Peters, Søren Buus
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引用次数: 48

摘要

免疫信息学是一门应用计算机科学方法来研究和模拟免疫系统的学科。免疫信息学解决的一个基本问题是如何理解MHC分子向T细胞递呈抗原的规则,这一过程是对感染和癌症的适应性免疫反应的核心。在个性化医疗的现代时代,建模和预测MHC可以呈现哪些抗原的能力是操纵免疫系统和设计治疗干预策略的关键。由于MHC是多基因和极端多态的,每个个体都拥有一组具有不同肽结合特异性的MHC分子,它们共同呈现出正在进行的蛋白质代谢的独特的个性化肽印记。绘制所有MHC同种异体是一项艰巨的任务,没有强大的生物信息学成分是无法实现的。因此,预测多肽- mhc结合的计算工具已成为T细胞表位发现的大多数管道中必不可少的工具,也是疫苗和癌症研究中不可避免的组成部分。在这里,我们描述了几个这样的工具的发展,从开创性的努力到目前最先进的方法,这些工具可以准确预测所有MHC分子的肽结合,甚至包括那些尚未在实验中表征的分子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Immunoinformatics: Predicting Peptide-MHC Binding.

Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.

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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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