A New Method for Recognizing Protein Complexes Based on Protein Interaction Networks and GO Terms

Abstract

Motivation: A protein complex is the combination of proteins which interact with each other. Protein–protein interaction (PPI) networks are composed of multiple protein complexes. It is very difficult to recognize protein complexes from PPI data due to the noise of PPI.Results: We proposed a new method, called Topology and Semantic Similarity Network (TSSN), based on topological structure characteristics and biological characteristics to construct the PPI. Experiments show that the TSSN can filter the noise of PPI data. We proposed a new algorithm, called Neighbor Nodes of Proteins (NNP), for recognizing protein complexes by considering their topology information. Experiments show that the algorithm can identify more protein complexes and more accurately. The recognition of protein complexes is vital in research on evolution analysis.Availability and implementation: https://github.com/bioinformatical-code/NNP.

An Ensemble Learning Framework for Detecting Protein Complexes From PPI Networks

“…ICJointLE ( Zhang et al, 2019 ) is a novel method to identify protein complexes with the features of joint colocalization and joint coexpression in static PPI networks. NNP ( Zhang et al, 2021 ) is a new method for recognizing protein complexes by topological characteristics and biological characteristics. Some methods ( Zaki et al, 2013 ; Wang et al, 2019 ) are based on topological information to weight interactions in PPI networks.…”