title: TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition.
abstract: Antifreeze proteins (AFPs) are indispensable for living organisms to survive in an extremely cold environment and have a variety of potential biotechnological applications. The accurate prediction of antifreeze proteins has become an important issue and is urgently needed. Although considerable progress has been made, AFP prediction is still a challenging problem due to the diversity of species. In this study, we proposed a new sequence-based AFP predictor, called TargetFreeze. TargetFreeze utilizes an enhanced feature representation method that weightedly combines multiple protein features and takes the powerful support vector machine as the prediction engine. Computer experiments on benchmark datasets demonstrate the superiority of the proposed TargetFreeze over most recently released AFP predictors. We also implemented a user-friendly web server, which is openly accessible for academic use and is available at http://csbio.njust.edu.cn/bioinf/TargetFreeze. TargetFreeze supplements existing AFP predictors and will have potential applications in AFP-related biotechnology fields.
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, China.
authors.forename: Xue
authors.initials: X
authors.lastname: He
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, China.
authors.forename: Ke
authors.initials: K
authors.lastname: Han
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Hu
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Yan
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, China.
authors.forename: Jing-Yu
authors.initials: JY
authors.lastname: Yang
authors.affiliation: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai, 200240, China.
authors.forename: Hong-Bin
authors.initials: HB
authors.lastname: Shen
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, China. njyudj@njust.edu.cn.|Changshu Institute, Nanjing University of Science and Technology, Changshu, 215513, China. njyudj@njust.edu.cn.
authors.forename: Dong-Jun
authors.initials: DJ
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D021301:Antifreeze Proteins / Q000737:chemistry* / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D019143:Evolution, Molecular; D056510:Position-Specific Scoring Matrices; D012372:ROC Curve; D015203:Reproducibility of Results; D012984:Software; D064878:Web Browser; D057188:Workflow
chemical_list: D000596:Amino Acids; D021301:Antifreeze Proteins
keywords: Antifreeze protein prediction; Machine learning; Multi-view protein features; Support vector machine