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 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.