TITLE:
pLoc_Deep-mGneg: Predict Subcellular Localization of Gram Negative Bacterial Proteins by Deep Learning
AUTHORS:
Xin-Xin Liu, Kuo-Chen Chou
KEYWORDS:
Pandemic Coronavirus, Multi-Label System, Gram Negative Bacterial Proteins, Learning at Deeper Level, Five-Steps Rule, PseAAC
JOURNAL NAME:
Advances in Bioscience and Biotechnology,
Vol.11 No.5,
May
11,
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 Gram negative bacterial protein subcellular
localization is vitally important. In view of this, a CNN based protein
subcellular localization predictor called “pLoc_Deep-mGnet” 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 98% and its local
accuracy is around 94% - 100%. Both are transcending other existing
state-of-the-art predictors significantly. 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-mGneg/, which will become a very useful tool for fighting pandemic coronavirus
and save the mankind of this planet.