Yu Jingkai, Fotouhi Farshad
Department of Computer Science, Wayne State University Detroit, Michigan, USA.
J Med Syst. 2006 Feb;30(1):39-44. doi: 10.1007/s10916-006-7402-3.
Discovery of the protein interactions that take place within a cell can provide a starting point for understanding biological regulatory pathways. Global interaction patterns among proteins, for example, can suggest new drug targets and aid the design of new drugs by providing a clearer picture of the biological pathways in the neighborhoods of the drug targets. High-throughput experimental screens have been developed to detect protein-protein interactions, however, they show high rates of errors in terms of false positives and false negatives. Many computational approaches have been proposed to tackle the problem of protein-protein interaction prediction. They range from comparative genomics based methods to data integration based approaches. Challenging properties of protein-protein interaction data have to be addressed appropriately before a higher quality interaction map with better coverage can be achieved. This paper presents a survey of major works in computational prediction of protein-protein interactions, explaining their assumptions, main ideas, and limitations.
发现细胞内发生的蛋白质相互作用可为理解生物调节途径提供一个起点。例如,蛋白质之间的全局相互作用模式可以通过更清晰地描绘药物靶点附近的生物途径来提示新的药物靶点,并有助于新药设计。已经开发出高通量实验筛选方法来检测蛋白质-蛋白质相互作用,然而,它们在假阳性和假阴性方面显示出很高的错误率。已经提出了许多计算方法来解决蛋白质-蛋白质相互作用预测问题。它们的范围从基于比较基因组学的方法到基于数据整合的方法。在获得具有更好覆盖范围的更高质量相互作用图谱之前,必须适当解决蛋白质-蛋白质相互作用数据具有挑战性的特性。本文对蛋白质-蛋白质相互作用计算预测的主要工作进行了综述,解释了它们的假设、主要思想和局限性。