Graduate School of Informatics, Department of Health Informatics, Middle East Technical University , 06800 Ankara, Turkey.
Chem Rev. 2016 Apr 27;116(8):4884-909. doi: 10.1021/acs.chemrev.5b00683. Epub 2016 Apr 13.
Identification of protein-protein interactions (PPIs) is at the center of molecular biology considering the unquestionable role of proteins in cells. Combinatorial interactions result in a repertoire of multiple functions; hence, knowledge of PPI and binding regions naturally serve to functional proteomics and drug discovery. Given experimental limitations to find all interactions in a proteome, computational prediction/modeling of protein interactions is a prerequisite to proceed on the way to complete interactions at the proteome level. This review aims to provide a background on PPIs and their types. Computational methods for PPI predictions can use a variety of biological data including sequence-, evolution-, expression-, and structure-based data. Physical and statistical modeling are commonly used to integrate these data and infer PPI predictions. We review and list the state-of-the-art methods, servers, databases, and tools for protein-protein interaction prediction.
蛋白质-蛋白质相互作用(PPIs)的鉴定是分子生物学的核心,因为蛋白质在细胞中的作用是毋庸置疑的。组合相互作用产生了多种功能的组合;因此,对 PPI 和结合区域的了解自然有助于功能蛋白质组学和药物发现。鉴于在蛋白质组中发现所有相互作用的实验限制,蛋白质相互作用的计算预测/建模是在蛋白质组水平上进行完整相互作用的前提。这篇综述旨在提供蛋白质-蛋白质相互作用及其类型的背景知识。蛋白质相互作用的预测方法可以使用多种生物数据,包括序列、进化、表达和结构数据。物理和统计建模通常用于整合这些数据并推断蛋白质相互作用的预测。我们回顾并列出了用于蛋白质-蛋白质相互作用预测的最新方法、服务器、数据库和工具。