TITLE:
pLoc_Deep-mPlant: Predict Subcellular Localization of Plant Proteins by Deep Learning
AUTHORS:
Yu-Tao Shao, Xin-Xin Liu, Zhe Lu, Kuo-Chen Chou
KEYWORDS:
Pandemic Coronavirus, Multi-Label System, Plant Proteins, Learning at Deeper Level, Five-Steps Rule, PseAAC
JOURNAL NAME:
Natural Science,
Vol.12 No.5,
May
13,
2020
ABSTRACT: Current coronavirus pandemic has endangered mankind life. The
reported cases are increasing exponentially. Information of plant protein
subcellular localization can provide useful clues to develop antiviral drugs.
To cope with such a catastrophe, a CNN based plant protein subcellular
localization predictor called “pLoc_Deep-mPlant” 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 95% and its local accuracy is about 90%-100%. Both have substantially
exceeded theother existing state-of-the-art predictors. To maximize the convenience
for mostexperimental scientists, a user-friendly web-server for the new
predictor has been establishedathttp://www.jci-bioinfo.cn/pLoc_Deep-mPlant/, by which the majority of
experimentalscientists can easily obtain their desired data without the need to go
through themathematical details.