iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites

Abstract

Predicting protein subcellular localization is a challenging problem, particularly when query proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing methods can only be used to deal with the single-location proteins. Actually, multiple-location proteins should not be ignored because they usually bear some special functions worthy of our notice. By introducing the “multi-labeled learning” approach, a new predictor, called iLoc-Plant, has been developed that can be used to deal with the systems containing both single- and multiple-location plant proteins. As a demonstration, the jackknife cross-validation was performed with iLoc-Plant on a benchmark dataset of plant proteins classified into the following 12 location sites: (1) cell membrane, (2) cell wall, (3) chloroplast, (4) cytoplasm, (5) endoplasmic reticulum, (6) extracellular, (7) Golgi apparatus, (8) mitochondrion, (9) nucleus, (10) peroxisome, (11) plastid, and (12) vacuole, where some proteins belong to two or three locations but none has ≥25% pairwise sequence identity to any other in a same subset. The overall success rate thus obtained by iLoc-Plant was 71%, which is remarkably higher than those achieved by any existing predictors that also have the capacity to deal with such a stringent and complicated plant protein system. As a user-friendly web-server, iLoc-Plant is freely accessible to the public at the web-site http://icpr.jci.edu.cn/bioinfo/iLoc-Plant or http://www.jci-bioinfo.cn/iLoc-Plant. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematic equations presented in this paper for its integrity. It is anticipated that iLoc-Plant may become a useful bioinformatics tool for Molecular Cell Biology, Proteomics, Systems Biology, and Drug Development.