TITLE:
pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins
AUTHORS:
Xuan Xiao, Xiang Cheng, Shengchao Su, Qi Mao, Kuo-Chen Chou
KEYWORDS:
Multi-Target Drugs, Gene Ontology, Chou’s General PseAAC, ML-GKR, Chou’s Metrics
JOURNAL NAME:
Natural Science,
Vol.9 No.9,
September
22,
2017
ABSTRACT:
The basic unit in life is cell.It
contains many protein molecules located at its different organelles. The growth
and reproduction of a cell as well as most of its other biological functions
are performed via these proteins. But proteins in different organelles or
subcellular locations have different functions. Facingthe avalanche of
protein sequences generated in the postgenomic age, we are challenged to
develop high throughput tools for identifying the subcellular localization of
proteins based on their sequence information alone. Although considerable
efforts have been made in this regard, the problem is far apart from being
solved yet. Most existing methods can be used to deal with single-location
proteins only. Actually, proteins with multi-locations may have some special
biological functions that are particularly important for drug targets. Using
the ML-GKR (Multi-Label Gaussian Kernel Regression) method,we developed a
new predictor called “pLoc-mGpos” by in-depth extracting the key information
from GO (Gene Ontology) into the Chou’s general PseAAC (Pseudo Amino Acid
Composition)for predicting the subcellular localization of Gram-positive
bacterial proteins with both single and multiple location sites. Rigorous
cross-validation on a same stringent benchmark dataset indicated that the
proposed pLoc-mGpos predictor is remarkably superior to “iLoc-Gpos”, the
state-of-the-art predictor for the same purpose.To maximize the
convenience of most experimental scientists, a user-friendly web-server for the
new powerful predictor has been established at http://www.jci-bioinfo.cn/pLoc-mGpos/, by which users can easily get their
desired results without the need to go through the complicated mathematics
involved.