TITLE:
pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning
AUTHORS:
Yutao Shao, Kuo-Chen Chou
KEYWORDS:
Coronavirus, Multi-Label System, Eukaryotic Proteins, Deep Learning, Five-Steps Rule, PseAAC
JOURNAL NAME:
Natural Science,
Vol.12 No.6,
June
30,
2020
ABSTRACT:
Recently, the life of worldwide human beings has been endangering
by the spreading of pneu- monia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop
effective drugs against Coronavirus, knowledge of protein subcellular localization
is prerequisite. In 2019, a predictor called “pLoc_bal-mEuk” was developed for
identifying the subcellular localization of eukaryotic proteins. Its predicted
results are significantly better than its counterparts, particularly for those
proteins that may simultaneously occur or move between two or more subcellular
location sites. However, more efforts are definitely needed to further improve
itspower since pLoc_bal-mEuk was still not trained by a “deep learning”,
a very powerful technique developed recently. The present study was devoted to
incorporating the “deep- learning” technique and developed a new predictor called “pLoc_Deep-mEuk”. The global absolute true
rate achieved by the new predictor is over 81% and its local accuracy is over
90%. Both are overwhelmingly superior to its counterparts. Moreover, a
user-friendly web- server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mEuk/,
by which the majority of experimental scientists can easily get their desired
data.