pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins - Natural Science

NS > Vol.9 No.9, September 2017

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins ()

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DOI: 10.4236/ns.2017.99032 1,337 Downloads 2,740 Views Citations

Author(s)

Xuan Xiao^1,2, Xiang Cheng^1,2,3, Shengchao Su³, Qi Mao³, Kuo-Chen Chou^2,4,5

Affiliation(s)

¹Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China.
²The Gordon Life Science Institute, Boston, USA.
³College of Information Science and Technology, Donghua University, Shanghai, China.
⁴Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.
⁵Faculty of Computing and Information Technology in Rabigh, King Abdul Aziz University, Jeddah, Saudi Arabia.

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. Facing the 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.

KEYWORDS

Multi-Target Drugs, Gene Ontology, Chou’s General PseAAC, ML-GKR, Chou’s Metrics

Share and Cite:

Xiao, X. , Cheng, X. , Su, S. , Mao, Q. and Chou, K. (2017) pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins. Natural Science, 9, 330-349. doi: 10.4236/ns.2017.99032.

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