TITLE:
pLoc_Deep-mAnimal: A Novel Deep CNN-BLSTM Network to Predict Subcellular Localization of Animal Proteins
AUTHORS:
Yu-Tao Shao, Kuo-Chen Chou
KEYWORDS:
Pandemic Coronavirus, Multi-Label System, Animal Proteins, Learning at Deeper Level, Five Steps Rule, PseAAC
JOURNAL NAME:
Natural Science,
Vol.12 No.5,
May
14,
2020
ABSTRACT: Current
coronavirus pandemic has endangered mankind life. The reported cases are increasing
exponentially. Information of animal protein subcellular localization can
provide useful clues to develop antiviral drugs. To cope with such a
catastrophe, a CNN based animal protein subcellular localization predictor
called “pLoc_Deep-mAnimal” 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 92% and its local accuracy is over 95%. Both have
substantially exceeded the other existing state-of-the-art predictors. 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-mAnimal/, which
will become a very useful tool for fighting pandemic coronavirus and save the
mankind of this planet.