Essential proteins identification method based on four-order distances and subcellular localization information  

作　　者：卢鹏丽 钟雨 杨培实 Pengli Lu;Yu Zhong;Peishi Yang(School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China;School of Tianmen Vocational College,Tianmen 431700,China)

机构地区：[1]School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China [2]School of Tianmen Vocational College,Tianmen 431700,China

出　　处：《Chinese Physics B》2024年第1期765-772,共8页中国物理B（英文版）

基　　金：Project supported by the Gansu Province Industrial Support Plan (Grant No.2023CYZC-25);the Natural Science Foundation of Gansu Province (Grant No.23JRRA770);the National Natural Science Foundation of China (Grant No.62162040)。

摘　　要：Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction(PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.

关 键 词：protein–protein interaction(PPI)network essential proteins four-order distances subcellular localization information 

分 类 号：Q811.4[生物学—生物工程]