Using Protein-protein Interaction Network Information to Predict the Subcellular Locations of Proteins in Budding Yeast

Le-Le      Hu; Kai-Yan      Feng; Yu-Dong      Cai; Kuo-Chen      Chou

doi:10.2174/092986612800494066

Abstract

The information of protein subcellular localization is vitally important for in-depth understanding the intricate pathways that regulate biological processes at the cellular level. With the rapidly increasing number of newly found protein sequence in the Post-Genomic Age, many automated methods have been developed attempting to help annotate their subcellular locations in a timely manner. However, very few of them were developed using the protein-protein interaction (PPI) network information. In this paper, we have introduced a new concept called “tethering potential” by which the PPI information can be effectively fused into the formulation for protein samples. Based on such a network frame, a new predictor called Yeast-PLoc has been developed for identifying budding yeast proteins among their 19 subcellular location sites. Meanwhile, a purely sequence-based approach, called the “hybrid-property” method, is integrated into Yeast-PLoc as a fall-back to deal with those proteins without sufficient PPI information. The overall success rate by the jackknife test on the 4,683 yeast proteins in the training dataset was 70.25%. Furthermore, it was shown that the success rate by Yeast- PLoc on an independent dataset was remarkably higher than those by some other existing predictors, indicating that the current approach by incorporating the PPI information is quite promising. As a user-friendly web-server, Yeast-PLoc is freely accessible at http://yeastloc.biosino.org/.

Keywords: Jackknife test, location-tethering network, protein-protein interaction, subcellular location, tethering potential, Yeast-PLoc, Alzheimer's disease, cellular environment, computational methods, amino acid composition (AAC)