TITLE:
pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning
AUTHORS:
Yu-Tao Shao, Xin-Xin Liu, Zhe Lu, Kuo-Chen Chou
KEYWORDS:
Coronavirus; Multi-Label System; Human Proteins; Deep Learning; Five-Steps Rule; PseAAC
JOURNAL NAME:
Natural Science,
Vol.12 No.7,
July
27,
2020
ABSTRACT: Recently, the life of human beings around
the entire world has been endangering by the spreading of pneumonia-causing
virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs
against Coronavirus, knowledge of protein subcellular localization is
indispensable. In 2019, a predictor called “pLoc_bal-mHum” was developed for
identifying the subcellular localization of human 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
its power since pLoc_bal-mHum was still not trained by a “deep learning”, a
very powerful technique developed recently. The present study was devoted to
incorporate the “deep-learning” technique and develop a new predictor called
“pLoc_Deep-mHum”. 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-mHum/, which will become a very useful tool
for fighting pandemic coronavirus and save the mankind of this planet.