Strokach Alexey, Corbi-Verge Carles, Teyra Joan, Kim Philip M
Department of Computer Science, University of Toronto, Toronto, ON, Canada.
Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.
Methods Mol Biol. 2019;1851:1-17. doi: 10.1007/978-1-4939-8736-8_1.
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.
蛋白质的功能很大程度上由其三维结构以及它与其他蛋白质的相互作用所决定。蛋白质氨基酸序列的改变会通过扰乱蛋白质折叠和结合的能量景观来改变其功能。人们已经开发了许多工具来预测氨基酸变化的能量效应，这些工具利用描述蛋白质序列、蛋白质结构或两者的特征。这些工具可有多种应用，比如区分有害突变和良性突变，以及设计具有吸引人特性的蛋白质和肽。在本章中，我们将描述如何使用其中一种工具ELASPIC，在人类蛋白质-蛋白质相互作用网络的背景下，预测突变对蛋白质稳定性和蛋白质之间亲和力的影响。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变化。它既可以通过网络服务器使用，也可以作为独立应用程序使用。由于ELASPIC是使用同源模型而非晶体结构进行训练的，所以它可以应用于比传统方法更广泛的蛋白质范围。它可以利用预先计算的序列比对、同源模型和其他特征，从而大幅减少评估单个突变所需的时间，并使分析影响基因组中大多数蛋白质的数百万个突变变得可行。
