title: Prediction of multi-type membrane proteins in human by an integrated approach.
abstract: Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their types. However, it is very time-consuming and expensive for traditional biophysical methods to identify membrane protein types. Although some computational tools predicting membrane protein types have been developed, most of them can only recognize one kind of type. Therefore, they are not as effective as one membrane protein can have several types at the same time. To our knowledge, few methods handling multiple types of membrane proteins were reported. In this study, we proposed an integrated approach to predict multiple types of membrane proteins by employing sequence homology and protein-protein interaction network. As a result, the prediction accuracies reached 87.65%, 81.39% and 70.79%, respectively, by the leave-one-out test on three datasets. It outperformed the nearest neighbor algorithm adopting pseudo amino acid composition. The method is anticipated to be an alternative tool for identifying membrane protein types. New metrics for evaluating performances of methods dealing with multi-label problems were also presented. The program of the method is available upon request.
authors.affiliation: Institute of Systems Biology, Shanghai University, Shanghai, China; Department of Mathematics, Shaoyang University, Shaoyang, Hunan, China.
authors.forename: Guohua
authors.initials: G
authors.lastname: Huang
authors.affiliation: Graduate School of the Chinese Academy of Sciences, Beijing, China; State Key Laboratory of Medical Genomics, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
authors.forename: Yuchao
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: College of Information Engineering, Shanghai Maritime University, Shanghai, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Chen
authors.affiliation: Department of Biomedical Engineering, Tianjin University, Tianjin Key Lab of BME Measurement, Tianjin, China.
authors.forename: Ning
authors.initials: N
authors.lastname: Zhang
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, New York, United States of America.
authors.forename: Tao
authors.initials: T
authors.lastname: Huang
authors.affiliation: Institute of Systems Biology, Shanghai University, Shanghai, China.
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data; D030562:Databases, Protein; D006801:Humans; D008565:Membrane Proteins / Q000737:chemistry*; D015233:Models, Statistical; D025941:Protein Interaction Mapping; D017386:Sequence Homology, Amino Acid
chemical_list: D008565:Membrane Proteins