title: iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites.
abstract: Although numerous efforts have been made for predicting the subcellular locations of proteins based on their sequence information, it still remains as a challenging problem, particularly when query proteins may have the multiplex character, i.e., they simultaneously exist, or move between, two or more different subcellular location sites. Most of the existing methods were established on the assumption: a protein has one, and only one, subcellular location. Actually, recent evidence has indicated an increasing number of human proteins having multiple subcellular locations. This kind of multiplex proteins should not be ignored because they may bear some special biological functions worthy of our attention. Based on the accumulation-label scale, a new predictor, called iLoc-Hum, was developed for identifying the subcellular localization of human proteins with both single and multiple location sites. As a demonstration, the jackknife cross-validation was performed with iLoc-Hum on a benchmark dataset of human proteins that covers the following 14 location sites: centrosome, cytoplasm, cytoskeleton, endoplasmic reticulum, endosome, extracellular, Golgi apparatus, lysosome, microsome, mitochondrion, nucleus, peroxisome, plasma membrane, and synapse, where some proteins belong to two, three or four locations but none has 25% or higher pairwise sequence identity to any other in the same subset. For such a complicated and stringent system, the overall success rate achieved by iLoc-Hum was 76%, which is remarkably higher than that by any of the existing predictors that also have the capacity to deal with this kind of system. Further comparisons were also made via two independent datasets; all indicated that the success rates by iLoc-Hum were even more significantly higher than its counterparts. As a user-friendly web-server, iLoc-Hum is freely accessible to the public at  or . For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results by choosing either a straightforward submission or a batch submission, without the need to follow the complicated mathematical equations involved.
authors: Gordon Life Science Institute, San Diego, California 92130, USA. kcchou@gordonlifescience.org
authors: Kuo-Chen
authors: KC
authors: Chou
authors: Zhi-Cheng
authors: ZC
authors: Wu
authors: Xuan
authors: X
authors: Xiao
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D019295:Computational Biology; D030562:Databases, Protein; D006801:Humans; D008962:Models, Theoretical; D011506:Proteins / Q000378:metabolism*; D020539:Sequence Analysis, Protein; D013347:Subcellular Fractions / Q000378:metabolism*
chemical_list: D011506:Proteins