Two new methods for identifying proteins based on the domain protein complexes and topological properties

TL;DR

This paper introduces two novel methods, CDC and CIBD, that leverage local complex and topological properties of protein networks to improve the identification of essential proteins in biological systems.

Contribution

The paper presents two new methods, CDC and CIBD, integrating complex and topological features for better essential protein prediction.

Findings

CDC and CIBD improve prediction precision

Methods validated on multiple PPI networks

Enhanced identification of essential proteins

Abstract

The recognition of essential proteins not only can help to understand the mechanism of cell operation, but also help to study the mechanism of biological evolution. At present, many scholars have been discovering essential proteins according to the topological structure of protein network and complexes. While some proteins still can not be recognized. In this paper, we proposed two new methods complex degree centrality (CDC) and complex in-degree and betweenness definition (CIBD) which integrate the local character of protein complexes and topological properties to determine the essentiality of proteins. First, we give the definitions of complex average centrality (CAC) and complex hybrid centrality (CHC) which both describe the properties of protein complexes. Then we propose these new methods CDC and CIBD based on CAC and CHC definitions. In order to access these two methods, different Protein-Protein Interaction (PPI) networks of Saccharomyces cerevisiae, DIP, MIPS and YMBD are used as experimental materials. Experimental results in networks show that the methods of CDC and CIBD can help to improve the precision of predicting essential proteins.