Background/Objective: Knowledge of protein subcellular localization is vitally important
for both basic research and drug development. Facing the avalanche of protein sequences
emerging in the post-genomic age, it is urgent to develop computational tools for timely and effectively
identifying their subcellular localization based on the sequence information alone. Recently,
a predictor called “pLoc-mVirus” was developed for identifying the subcellular localization of virus
proteins. Its performance is overwhelmingly better than that of the other predictors for the
same purpose, particularly in dealing with multi-label systems in which some proteins, known as
“multiplex proteins”, may simultaneously occur in, or move between two or more subcellular location
sites. Despite the fact that it is indeed a very powerful predictor, more efforts are definitely
needed to further improve it. This is because pLoc-mVirus was trained by an extremely skewed
dataset in which some subset was over 10 times the size of the other subsets. Accordingly, it cannot
avoid the biased consequence caused by such an uneven training dataset.
Methods: Using the Chou's general PseAAC (Pseudo Amino Acid Composition) approach and the
IHTS (Inserting Hypothetical Training Samples) treatment to balance out the training dataset, we
have developed a new predictor called “pLoc_bal-mVirus” for predicting the subcellular localization
of multi-label virus proteins.
Results: Cross-validation tests on exactly the same experiment-confirmed dataset have indicated
that the proposed new predictor is remarkably superior to pLoc-mVirus, the existing state-of-theart
predictor for the same purpose.
Conclusion: Its user-friendly web-server is available at http://www.jci-bioinfo.cn/pLoc_balmVirus/,
by which the majority of experimental scientists can easily get their desired results without
the need to go through the detailed complicated mathematics. Accordingly, pLoc_bal-mVirus
will become a very useful tool for designing multi-target drugs and in-depth understanding of the
biological process in a cell.