title: Predicting bacteriophage proteins located in host cell with feature selection technique.
abstract: A bacteriophage is a virus that can infect a bacterium. The fate of an infected bacterium is determined by the bacteriophage proteins located in the host cell. Thus, reliably identifying bacteriophage proteins located in the host cell is extremely important to understand their functions and discover potential anti-bacterial drugs. Thus, in this paper, a computational method was developed to recognize bacteriophage proteins located in host cells based only on their amino acid sequences. The analysis of variance (ANOVA) combined with incremental feature selection (IFS) was proposed to optimize the feature set. Using a jackknife cross-validation, our method can discriminate between bacteriophage proteins located in a host cell and the bacteriophage proteins not located in a host cell with a maximum overall accuracy of 84.2%, and can further classify bacteriophage proteins located in host cell cytoplasm and in host cell membranes with a maximum overall accuracy of 92.4%. To enhance the value of the practical applications of the method, we built a web server called PHPred (〈http://lin.uestc.edu.cn/server/PHPred〉). We believe that the PHPred will become a powerful tool to study bacteriophage proteins located in host cells and to guide related drug discovery.
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China. Electronic address: hding@uestc.edu.cn.
authors.forename: Hui
authors.initials: H
authors.lastname: Ding
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Zhi-Yong
authors.initials: ZY
authors.lastname: Liang
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Feng-Biao
authors.initials: FB
authors.lastname: Guo
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Huang
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China; Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China. Electronic address: greatchen@heuu.edu.cn.
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China. Electronic address: hlin@uestc.edu.cn.
authors.forename: Hao
authors.initials: H
authors.lastname: Lin
mesh_terms: D001419:Bacteria / Q000378:metabolism* / Q000821:virology*; D001435:Bacteriophages / Q000378:metabolism*; D002462:Cell Membrane / Q000378:metabolism / Q000821:virology; D003593:Cytoplasm / Q000378:metabolism / Q000821:virology; D008954:Models, Biological; D014764:Viral Proteins / Q000378:metabolism*
chemical_list: D014764:Viral Proteins
keywords: Analysis of variance; Bacteriophage proteins; Feature analysis; g-gap dipeptide