Prediction of bacterial protein subcellular localization by incorporating various features into Chou's PseAAC and a backward feature selection approach.

Information on the subcellular localization of bacterial proteins is essential for protein function prediction, genome annotation and drug design. Here we proposed a novel approach to predict the subcellular localization of bacterial proteins by fusing features from position-specific score matrix (PSSM), Gene Ontology (GO) and PROFEAT. A backward feature selection approach by linear kennel of SVM was then used to rank the integrated feature vectors and extract optimal features. Finally, SVM was applied for predicting protein subcellular locations based on these optimal features. To validate the performance of our method, we employed jackknife cross-validation tests on three low similarity datasets, i.e., M638, Gneg1456 and Gpos523. The overall accuracies of 94.98%, 93.21%, and 94.57% were achieved for these three datasets, which are higher (from 1.8% to 10.9%) than those by state-of-the-art tools. Comparison results suggest that our method could serve as a very useful vehicle for expediting the prediction of bacterial protein subcellular localization.