TITLE:
pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning
AUTHORS:
Yutao Shao, Kuo-Chen Chou
KEYWORDS:
Coronavirus, Virus Proteins, Multi-Label System, Deep Learning, Five-Steps Rule, PseAAC
JOURNAL NAME:
Natural Science,
Vol.12 No.6,
June
23,
2020
ABSTRACT: The recent worldwide spreading of pneumonia-causing virus, such as
Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all
around the world. In order to really understand the biological process within a
cell level and provide useful clues to develop antiviral drugs, information of
virus protein subcellular localization is vitally important. In view of this, a
CNN based virus protein subcellular localization predictor called
“pLoc_Deep-mVirus” was developed. The predictor is particularly useful in
dealing with the multi-sites systems in which some proteins may simultaneously
occur in two or more different organelles that are the current focus of
pharmaceutical industry. The global absolute true rate achieved by the new
predictor is over 97% and its local accuracy is over 98%. Both are transcending
other existing state-of-the-art predictors significantly. It has not
escapedour notice that the deep-learning treatment can be used to deal
with many other biologicalsystems as well. 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_Deep-mVirus/.