PFP_Min: A graph Theoretic Approach for Prediction of Protein Function from Protein-Interaction Network

Abstract

Proteins are responsible for all biological activities in a living object. With the advent of genome sequencing projects for different organisms, large amounts of DNA and protein sequence data is available, whereas their biological function is still un-annotated in most of the cases. Predicting protein function is the most challenging problem in post-genomic era. Using sequence homology, phylogenetic profiles, gene expression data function of un-annotated protein can be predicted. Recently, the large interaction networks constructed from high-throughput techniques like Yeast2Hybrid experiments are also used in protein function. In this paper, a graph-theoretic approach PFP_Min is proposed for prediction of protein interaction network. This approach considers protein Interaction network as a graph with every protein being an individual node where some of them are assumed to be of unknown function. The objective is to assign function to un-annotated protein based on the minimum cut set. While assigning function to unknown protein, a neighborhood heuristic is also taken to achieve better prediction accuracy.