Chen Yu, Xu Dong
Protein Informatics Group, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
Curr Protein Pept Sci. 2003 Jun;4(3):159-81. doi: 10.2174/1389203033487225.
Protein-protein interactions play important roles in nearly all events that take place in a cell. High-throughput experimental techniques enable the study of protein-protein interactions at the proteome scale through systematic identification of physical interactions among all proteins in an organism. High-throughput protein-protein interaction data, with ever-increasing volume, are becoming the foundation for new biological discoveries. A great challenge to bioinformatics is to manage, analyze, and model these data. In this review, we describe several databases that store, query, and visualize protein-protein interaction data. Comparison between experimental techniques shows that each high-throughput technique such as yeast two-hybrid assay or protein complex identification through mass spectrometry has its limitations in detecting certain types of interactions and they are complementary to each other. In silico methods using protein/DNA sequences, domain and structure information to predict protein-protein interaction can expand the scope of experimental data and increase the confidence of certain protein-protein interaction pairs. Protein-protein interaction data correlate with other types of data, including protein function, subcellular location, and gene expression profile. Highly connected proteins are more likely to be essential based on the analyses of the global architecture of large-scale interaction network in yeast. Use of protein-protein interaction networks, preferably in conjunction with other types of data, allows assignment of cellular functions to novel proteins and derivation of new biological pathways. As demonstrated in our study on the yeast signal transduction pathway for amino acid transport, integration of high-throughput data with traditional biology resources can transform the protein-protein interaction data from noisy information into knowledge of cellular mechanisms.
蛋白质-蛋白质相互作用在细胞内几乎所有发生的事件中都起着重要作用。高通量实验技术能够通过系统鉴定生物体中所有蛋白质之间的物理相互作用，在蛋白质组规模上研究蛋白质-蛋白质相互作用。高通量蛋白质-蛋白质相互作用数据的量不断增加，正成为新生物学发现的基础。生物信息学面临的一个巨大挑战是管理、分析和建模这些数据。在本综述中，我们描述了几个存储、查询和可视化蛋白质-蛋白质相互作用数据的数据库。实验技术之间的比较表明，每种高通量技术，如酵母双杂交试验或通过质谱鉴定蛋白质复合物，在检测某些类型的相互作用方面都有其局限性，它们相互补充。利用蛋白质/DNA序列、结构域和结构信息预测蛋白质-蛋白质相互作用的计算机方法可以扩展实验数据的范围，并增加某些蛋白质-蛋白质相互作用对的可信度。蛋白质-蛋白质相互作用数据与其他类型的数据相关，包括蛋白质功能、亚细胞定位和基因表达谱。基于对酵母大规模相互作用网络全局架构的分析，高度连接的蛋白质更有可能是必需的。使用蛋白质-蛋白质相互作用网络，最好结合其他类型的数据，可以将细胞功能赋予新蛋白质，并推导新的生物学途径。正如我们在酵母氨基酸转运信号转导途径研究中所证明的，将高通量数据与传统生物学资源整合可以将蛋白质-蛋白质相互作用数据从嘈杂的信息转化为细胞机制的知识。
