A Unified Scoring Scheme for Detecting Essential Proteins in Protein Interaction Networks

Abstract

The essentiality of a gene or protein is important for understanding the minimal requirements for cellular survival and development. Numerous computational methodologies have been proposed to detect essential proteins from large protein-protein interactions (PPI) datasets. However, only a handful of overlapping essential proteins exists between them. This suggests that the methods may be complementary and an integration scheme which exploits the differences should better detect essential proteins. We introduce a novel algorithm, UniScore, which combines predictions produced by existing methods. Experimental results on four Saccharomyces cerevisiae PPI datasets showed that UniScore consistently produced significantly better predictions and substantially outperforming SVM which is one of the most popular and advanced classification technique. In addition, previously hard-to-detect low-connectivity essential proteins have also been identified by UniScore.