一种基于多层PPI网络的关键蛋白质识别方法

Identification of Essential Proteins Based on Multilayer Protein-Protein Interaction Network

关键蛋白质是生物体存活和繁育后代所必需的重要物质,通过蛋白质相互作用网络识别关键蛋白质是当前生物信息学研究的热点.现有方法主要通过整合蛋白质相互作用数据和基因表达水平时间序列数据来提高关键蛋白质识别率,但是这些方法通常基于单层网络进行中心性计算和关键蛋白质识别,没有充分挖掘基因表达水平数据的时序特性.本文提出一种基于多层网络的关键蛋白质识别方法,该方法首先构建各观测时点的活跃蛋白质相互作用子网络,并对各个网络的活跃蛋白质进行中心性计算,然后将每个蛋白质所有时点的中心性值进行加权求和,最后计算前100到600蛋白质中包含的关键蛋白质数量.实验表明,该方法比现有单层网络方法具有更高的识别率.

Essential proteins are important substances necessary for the survival and breeding of organisms.Identifying essential proteins through protein interaction networks is a hot spot in current bioinformatics research.Existing methods mainly improve the recognition rate of essential proteins by integrating protein interaction data and time series data of gene expression levels.However,these methods are usually based on single-layer networks for centrality calculations and essential protein identification.The temporal characteristics of gene expression level data are not fully explored.Based on the above reasons,we propose a multi-layer network-based essential protein recognition method.This method constructs the active protein interaction sub-networks at each observation time point at first,and then calculates the centrality of the active proteins of each network.After that,the centrality values of all time points for each protein are weighted and summed.Finally,calculate the number of key proteins contained in the top 100 to 600 proteins of centrality.Experiments show that the proposed method has a higher recognition rate of essential proteins than existing single-layer network methods.