SIPEC: Systematic identification of self-interacting proteins with ensemble classifiers using evolutionary information

Lei Wang,Tao Wang,Shan-Wen Zhang,Z.-H You,Li-Ping Li,Ya-Ping Wu

doi:10.18632/oncotarget.24141

Abstract

SIPEC: Systematic identification of self-interacting proteins with ensemble classifiers using evolutionary information

Highlights

As both the material base of life and the main bearer of life activities, proteins affect the cells through interaction with other components
Ispolatov et al [10] noted that Self-interacting Proteins (SIPs) occupy a significant position in the protein interaction networks (PINs), meaning that there are great possibilities that the SIPs can interact with a large number of other proteins
We proposed a novel computational method based on protein sequence information to largescale and efficient prediction protein self-interaction, which combines the feature extraction method Auto Covariance (AC) and improved rotation forest classifier

Summary

Introduction

As both the material base of life and the main bearer of life activities, proteins affect the cells through interaction with other components. In these interactions, Protein-Protein Interactions (PPIs) has attracted more attention of researchers because of their critical roles in living organisms. One special type of PPIs is Self-interacting proteins (SIPs) They represent those with more than two copies that can interact with each other. Pereira-Leal and their collaborators proposed a genome-wide, cross-species analysis of the origins and evolution of protein complexes Their conclusion indicates that the evolution of many protein complexes was first established through self-interactions and through the duplication of these self-interacting proteins [11]. Without increasing the size of the genome, through self-interactions, the functional diversity of proteins can be greatly expanded [12]

Methods

Results

Conclusion