NetLoc: Network based protein localization prediction using protein-protein interaction and co-expression networks

Abstract

Recent studies showed that protein-protein interaction network based features can significantly improve the prediction of protein subcellular localization. However, it is unclear whether network prediction models or other types of protein-protein correlation networks would also improve localization prediction. We present NetLoc, a novel diffusion kernel-based logistic regression (KLR) algorithm for predicting protein subcellular localization using four types of protein networks including physical protein-protein interaction (PPPI) networks, genetic PPI networks (GPPI), mixed PPI networks (MPPI), and co-expression networks (COEXP). We applied NetLoc to yeast protein localization prediction. The results showed that protein networks can provide rich information for protein localization prediction, achieving prediction performance up to AUC score of 0.93. We also showed that networks with high connectivity and high percentage of interacting protein pairs targeting the same location lead to better prediction performance. We found that physical PPPI is better than GPPI which is better than COEXP in terms of localization prediction. The prediction performance (AUC) using the yeast PPPI network ranges between 0.71 and 0.93 for 7 locations. Compared to the previous network feature based prediction algorithm which achieved AUC scores of (0.49 and 0.52) on the yeast PPI network of the DIP database, NetLoc achieved significantly better overall performance with the AUC of 0.74.