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

作     者：Xuan Xiao Xiang Cheng Shengchao Su Qi Mao Kuo-Chen Chou 

作者机构：Computer Department Jingdezhen Ceramic Institute Jingdezhen China The Gordon Life Science Institute Boston USA College of Information Science and Technology Donghua University Shanghai China Center for Informational Biology University of Electronic Science and Technology of China Chengdu China Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Jeddah Saudi Arabia 

出 版 物：《Natural Science》 (自然科学期刊（英文）)

年 卷 期：2017年第9卷第9期

页      面：330-349页

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主　　题：Multi-Target Drugs Gene Ontology Chou’s General PseAAC ML-GKR Chou’s Metrics 

摘      要：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://***/pLoc-mGpos/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.