Predicting the Effect of Mutations on Protein Folding and Protein-Protein Interactions
Predicting the Effect of Mutations on Protein Folding and Protein-Protein Interactions
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DOI：
10.1007/978-1-4939-8736-8_1
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发表时间：
2019-01-01
期刊：
影响因子：
--
通讯作者：
Kim, Philip M.
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文献类型：
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作者：
Strokach, Alexey;Corbi-Verge, Carles;Kim, Philip M.
蛋白质的功能在很大程度上取决于其三维结构及其与其他蛋白质的相互作用。蛋白质氨基酸序列的改变可以通过扰乱蛋白质折叠和结合的能量景观来改变其功能。已经开发了许多工具来预测氨基酸变化的能量效应，利用描述蛋白质序列、蛋白质结构或两者的特征。这些工具可以有许多应用，例如区分有害和良性突变以及设计具有吸引力特性的蛋白质和肽。在本章中，我们将介绍如何使用这样的工具之一，ELASPIC，预测突变对蛋白质的稳定性和蛋白质之间的亲和力的影响，在人类蛋白质-蛋白质相互作用网络的背景下。ELASPIC使用广泛的序列和结构特征来预测蛋白质折叠和蛋白质-蛋白质相互作用的吉布斯自由能的变化。它既可以通过Web服务器使用，也可以作为独立的应用程序使用。由于ELASPIC是使用同源模型而不是晶体结构进行训练的，因此它可以应用于比传统方法更广泛的蛋白质。它可以利用预先计算的序列比对，同源性模型和其他功能，以大大降低评估单个突变所需的时间，并使影响基因组中大多数蛋白质的数百万突变的分析变得容易。
The function of a protein is largely determined by its three-dimensional structure and its interactions with other proteins. Changes to a protein's amino acid sequence can alter its function by perturbing the energy landscapes of protein folding and binding. Many tools have been developed to predict the energetic effect of amino acid changes, utilizing features describing the sequence of a protein, the structure of a protein, or both. Those tools can have many applications, such as distinguishing between deleterious and benign mutations and designing proteins and peptides with attractive properties. In this chapter, we describe how to use one of such tools, ELASPIC, to predict the effect of mutations on the stability of proteins and the affinity between proteins, in the context of a human protein-protein interaction network. ELASPIC uses a wide range of sequential and structural features to predict the change in the Gibbs free energy for protein folding and protein-protein interactions. It can be used both through a web server and as a stand-alone application. Since ELASPIC was trained using homology models and not crystal structures, it can be applied to a much broader range of proteins than traditional methods. It can leverage precalculated sequence alignments, homology models, and other features, in order to drastically lower the amount of time required to evaluate individual mutations and make tractable the analysis of millions of mutations affecting the majority of proteins in a genome.