Determining protein-protein functional associations by functional rules based on gene ontology and KEGG pathway.

Yu-Hang Zhang, Tao Zeng, Lei Chen, Tao Huang, Yu-Dong Cai

Journal: Biochimica et biophysica acta. Proteins and proteomics 2021;1869(6):140621

PMID: 33561576

Abstract

Protein-protein interactions (PPIs) describe the direct physical contact of two proteins that usually results in specific biological functions or regulatory processes. The characterization and study of PPIs through the investigation of their pattern and principle have remained a question in biological studies. Various experimental and computational methods have been used for PPI studies, but most of them are based on the sequence similarity with current validated PPI participators or cellular localization patterns. Most methods ignore the fact that PPIs are defined by their specific biological functions. In this study, we constructed a novel rule-based computational method using gene ontology and KEGG pathway annotation of PPI participators that correspond to the complicated biological effects of PPIs. Our newly presented computational method identified a group of biological functions that are tightly associated with PPIs and provided a new function-based tool for PPI studies in a rule manner.

Copyright © 2021. Published by Elsevier B.V.

Address: School of Life Sciences, Shanghai University, Shanghai 200444, China; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: [email protected].; CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China. Electronic address: [email protected].; College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China. Electronic address: [email protected].; Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China. Electronic address: [email protected].; School of Life Sciences, Shanghai University, Shanghai 200444, China. Electronic address: [email protected].

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