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 or . 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
