Prediction of protein subcellular localization by incorporating sequence and protein-protein interaction features

Information of protein subcellular localization is indispensable to study protein function, as a protein can perform its function only after it is correctly transported to a specific subcellular compartment. Thus it is very important to provide accurate prediction of protein subcellular localization in biological studies. In contrast to sequence features (e.g. amino acids composition) that are widely used in subcellular localization prediction, features extracting protein-protein interaction (PPI) are largely ignored, although they reflect the co-localization information of different proteins. In this study, we propose a novel distance formula based on both protein sequence and PPI features, which precisely measures the similarity of proteins by incorporating protein information including amino acid composition, PPI and the corresponding interaction scores. Based on this distance formula, we further introduce a k-nearest neighbor (KNN) algorithm for predicting subcellular localization. The results of leave-one-out test on a benchmark dataset show that PPI features significantly improve the performance of protein subcellular localization. Meanwhile, this KNN algorithm also outperformes SVM algorithm adopting the same features, suggesting the efficiency of the proposed algorithm for predicting protein subcellular localization. ©, 2015, Univ. of Electronic Science and Technology of China. All right reserved.