Objective: Information of protein subcellular localization is crucially important for both
basic research and drug development. With the explosive growth of protein sequences discovered
in the post-genomic age, it is highly demanded to develop powerful bioinformatics tools for timely
and effectively identifying their subcellular localization purely based on the sequence information
alone. Recently, a predictor called “pLoc-mEuk” was developed for identifying the subcellular localization
of eukaryotic proteins. Its performance is overwhelmingly better than that of the other
predictors for the same purpose, particularly in dealing with multi-label systems where many proteins,
called “multiplex proteins”, may simultaneously occur in two or more subcellular locations.
Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve
it. This is because pLoc-mEuk was trained by an extremely skewed dataset where some subset
was about 200 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence
caused by such an uneven training dataset.
Methods: To alleviate such bias, we have developed a new predictor called pLoc_bal-mEuk by
quasi-balancing the training dataset. Cross-validation tests on exactly the same experimentconfirmed
dataset have indicated that the proposed new predictor is remarkably superior to pLocmEuk,
the existing state-of-the-art predictor in identifying the subcellular localization of eukaryotic
proteins. It has not escaped our notice that the quasi-balancing treatment can also be used to deal
with many other biological systems.
Results: To maximize the convenience for most experimental scientists, a user-friendly web-server
for the new predictor has been established at http://www.jci-‐bioinfo.cn/pLoc_bal-‐mEuk/.
Conclusion: It is anticipated that the pLoc_bal-Euk predictor holds very high potential to become
a useful high throughput tool in identifying the subcellular localization of eukaryotic proteins, particularly
for finding multi-target drugs that is currently a very hot trend trend in drug development.