M-Finder: Uncovering functionally associated proteins from interactome data integrated with GO annotations

Young-Rae Cho,Pietro H Guzzi,Yanxin Lu,Nayoung Kwon,Marco Mina

doi:10.1186/1477-5956-11-s1-s3

Abstract

BackgroundProtein-protein interactions (PPIs) play a key role in understanding the mechanisms of cellular processes. The availability of interactome data has catalyzed the development of computational approaches to elucidate functional behaviors of proteins on a system level. Gene Ontology (GO) and its annotations are a significant resource for functional characterization of proteins. Because of wide coverage, GO data have often been adopted as a benchmark for protein function prediction on the genomic scale.ResultsWe propose a computational approach, called M-Finder, for functional association pattern mining. This method employs semantic analytics to integrate the genome-wide PPIs with GO data. We also introduce an interactive web application tool that visualizes a functional association network linked to a protein specified by a user. The proposed approach comprises two major components. First, the PPIs that have been generated by high-throughput methods are weighted in terms of their functional consistency using GO and its annotations. We assess two advanced semantic similarity metrics which quantify the functional association level of each interacting protein pair. We demonstrate that these measures outperform the other existing methods by evaluating their agreement to other biological features, such as sequence similarity, the presence of common Pfam domains, and core PPIs. Second, the information flow-based algorithm is employed to discover a set of proteins functionally associated with the protein in a query and their links efficiently. This algorithm reconstructs a functional association network of the query protein. The output network size can be flexibly determined by parameters.ConclusionsM-Finder provides a useful framework to investigate functional association patterns with any protein. This software will also allow users to perform further systematic analysis of a set of proteins for any specific function. It is available online at http://bionet.ecs.baylor.edu/mfinder

Highlights

Protein-protein interactions (PPIs) play a key role in understanding the mechanisms of cellular processes
A functional module represents a set of proteins which participate in the same biological processes
There exist some unreliable sources on Gene Ontology (GO) annotation data, they are often adopted as a benchmark for functional characterization of proteins because of their wide coverage on the genomic scale over various model organisms

Summary

Introduction

Protein-protein interactions (PPIs) play a key role in understanding the mechanisms of cellular processes. The availability of interactome data has catalyzed the development of computational approaches to elucidate functional behaviors of proteins on a system level. GO data have often been adopted as a benchmark for protein function prediction on the genomic scale. As one of the integrative approaches, the functional association level (or functional consistency) between interacting proteins can be quantified by a semantic similarity measure which represents a model for measuring closeness in meaning between two or more ontological terms. There exist some unreliable sources on GO annotation data (e.g., the results from high-throughput experimental and computational methods), they are often adopted as a benchmark for functional characterization of proteins because of their wide coverage on the genomic scale over various model organisms

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