Chautard E, Thierry-Mieg N, Ricard-Blum S
UMR 5086 CNRS, institut de biologie et chimie des protéines, université Lyon 1, IFR, 128 biosciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon cedex 07, France.
Pathol Biol (Paris). 2009 Jun;57(4):324-33. doi: 10.1016/j.patbio.2008.10.004. Epub 2008 Dec 13.
Most genes, proteins and other components carry out their functions within a complex network of interactions and a single molecule can affect a wide range of other cell components. A global, integrative, approach has been developed for several years, including protein-protein interaction networks (interactomes). In this review, we describe the high-throughput methods used to identify new interactions and to build large interaction datasets. The minimum information required for reporting a molecular interaction experiment (MIMIx) has been defined as a standard for storing data in publicly available interaction databases. Several examples of interaction networks from molecular machines (proteasome) or organelles (phagosome, mitochondrion) to whole organisms (viruses, bacteria, yeast, fly, and worm) are given and attempts to cover the entire human interaction network are discussed. The methods used to perform the topological analysis of interaction networks and to extract biological information from them are presented. These investigations have provided clues on protein functions, signalling and metabolic pathways, and physiological processes, unraveled the molecular basis of some diseases (cancer, infectious diseases), and will be very useful to identify new therapeutic targets and for drug discovery. A major challenge is now to integrate data from different sources (interactome, transcriptome, phenome, localization) to switch from static to dynamic interaction networks. The merging of a viral interactome and the human interactome has been used to simulate viral infection, paving the way for future studies aiming at providing molecular basis of human diseases.
大多数基因、蛋白质和其他成分在复杂的相互作用网络中发挥其功能，单个分子可以影响广泛的其他细胞成分。一种全面、综合的方法已经发展了数年，包括蛋白质-蛋白质相互作用网络（相互作用组）。在这篇综述中，我们描述了用于识别新相互作用和构建大型相互作用数据集的高通量方法。报告分子相互作用实验所需的最小信息（MIMIx）已被定义为在公开可用的相互作用数据库中存储数据的标准。给出了从分子机器（蛋白酶体）或细胞器（吞噬体、线粒体）到整个生物体（病毒、细菌、酵母、果蝇和线虫）的相互作用网络的几个例子，并讨论了覆盖整个人类相互作用网络的尝试。介绍了用于对相互作用网络进行拓扑分析并从中提取生物学信息的方法。这些研究为蛋白质功能、信号传导和代谢途径以及生理过程提供了线索，揭示了一些疾病（癌症、传染病）的分子基础，并且对于识别新的治疗靶点和药物发现将非常有用。现在一个主要挑战是整合来自不同来源的数据（相互作用组、转录组、表型组、定位），以便从静态相互作用网络转向动态相互作用网络。病毒相互作用组和人类相互作用组的合并已被用于模拟病毒感染，为未来旨在提供人类疾病分子基础的研究铺平了道路。
