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网络分析和基于计算机的蛋白质-蛋白质相互作用预测及其在药物发现中的应用。

Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery.

机构信息

National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan.

National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan.

出版信息

Curr Opin Struct Biol. 2017 Jun;44:134-142. doi: 10.1016/j.sbi.2017.02.005. Epub 2017 Mar 30.

Abstract

Protein-protein interactions (PPIs) are vital to maintaining cellular homeostasis. Several PPI dysregulations have been implicated in the etiology of various diseases and hence PPIs have emerged as promising targets for drug discovery. Surface residues and hotspot residues at the interface of PPIs form the core regions, which play a key role in modulating cellular processes such as signal transduction and are used as starting points for drug design. In this review, we briefly discuss how PPI networks (PPINs) inferred from experimentally characterized PPI data have been utilized for knowledge discovery and how in silico approaches to PPI characterization can contribute to PPIN-based biological research. Next, we describe the principles of in silico PPI prediction and survey the existing PPI and PPI site prediction servers that are useful for drug discovery. Finally, we discuss the potential of in silico PPI prediction in drug discovery.

摘要

蛋白质-蛋白质相互作用(PPIs)对于维持细胞内稳态至关重要。几种 PPI 的失调与各种疾病的病因有关,因此 PPI 已成为药物发现的有前途的靶点。PPIs 界面处的表面残基和热点残基形成核心区域,在调节细胞过程(如信号转导)中发挥关键作用,并被用作药物设计的起点。在这篇综述中,我们简要讨论了如何利用实验表征的 PPI 数据推断的 PPI 网络(PPIN)进行知识发现,以及计算方法在 PPI 特征描述中的应用如何有助于基于 PPIN 的生物研究。接下来,我们描述了计算 PPI 预测的原理,并调查了现有的用于药物发现的 PPI 和 PPI 位点预测服务器。最后,我们讨论了计算 PPI 预测在药物发现中的潜力。

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