iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites

In the last two decades or so, although many computational methods were developed for predicting the subcellular locations of proteins according to their sequence information, it is still remains as a challenging problem, particularly when the system concerned contains both single- and multiple-location proteins. Also, among the existing methods, very few were developed specialized for dealing with viral proteins, those generated by viruses. Actually, knowledge of the subcellular localization of viral proteins in a host cell or virus-infected cell is very important because it is closely related to their destructive tendencies and consequences. In this paper, by introducing the "multi-label scale" and by hybridizing the gene ontology information with the sequential evolution information, a predictor called iLoc-Virus is developed. It can be utilized to identify viral proteins among the following six locations: (1) viral capsid, (2) host cell membrane, (3) host endoplasmic reticulum, (4) host cytoplasm, (5) host nucleus, and (6) secreted. The iLoc-Virus predictor not only can more accurately predict the location sites of viral proteins in a host cell, but also have the capacity to deal with virus proteins having more than one location. As a user-friendly web-server, iLoc-Virus is freely accessible to the public at http://icpr.jci.edu.cn/bioinfo/iLoc-Virus. Meanwhile, a step-by-step guide is provided on how to use the web-server to get the desired results. Furthermore, for the user's convenience, the iLoc-Virus web-server also has the function to accept the batch job submission. It is anticipated that iLoc-Virus may become a useful high throughput tool for both basic research and drug development.