Virus-mPLoc: A Fusion Classifier for Viral Protein Subcellular Location Prediction by Incorporating Multiple Sites

Abstract

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. Facing the avalanche of new protein sequences discovered in the post genomic era, we are challenged to develop automated methods for quickly and accurately predicting the location sites of viral proteins in a host cell; the information thus acquired is particularly important for medical science and antiviral drug design. In view of this, a new fusion classifier called “Virus-mPLoc” was established by hybridizing the gene ontology information, functional domain information, and sequential evolutionary information. The new predictor not only can more accurately predict the location sites of viral proteins in a host cell, but also have the capacity to identify the multiple-location virus proteins, which is beyond the reach of any existing predictors specialized for viral proteins. For reader's convenience, a user- friendly web-server for Virus-mPLoc was designed that is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/virus-multi/.

Key words:

• Virus-infected cell
• Multiplex protein
• PseAAC
• Gene ontology
• Functional domain
• Sequential evolution
• OET-KNN
• Fusion approach