Genome-scale prediction of moonlighting proteins using diverse protein association information.

ABSTRACT:

Motivation

Moonlighting proteins (MPs) show multiple cellular functions within a single polypeptide chain. To understand the overall landscape of their functional diversity, it is important to establish a computational method that can identify MPs on a genome scale. Previously, we have systematically characterized MPs using functional and omics-scale information. In this work, we develop a computational prediction model for automatic identification of MPs using a diverse range of protein association information.

Results

We incorporated a diverse range of protein association information to extract characteristic features of MPs, which range from gene ontology (GO), protein-protein interactions, gene expression, phylogenetic profiles, genetic interactions and network-based gra

SUBMITTER: Khan IK 

PROVIDER: S-EPMC4965633 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature