title: Identification of antifreeze proteins and their functional residues by support vector machine and genetic algorithms based on n-peptide compositions.
abstract: For the first time, multiple sets of n-peptide compositions from antifreeze protein (AFP) sequences of various cold-adapted fish and insects were analyzed using support vector machine and genetic algorithms. The identification of AFPs is difficult because they exist as evolutionarily divergent types, and because their sequences and structures are present in limited numbers in currently available databases. Our results reveal that it is feasible to identify the shared sequential features among the various structural types of AFPs. Moreover, we were able to identify residues involved in ice binding without requiring knowledge of the three-dimensional structures of these AFPs. This approach should be useful for genomic and proteomic studies involving cold-adapted organisms.
authors.affiliation: Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan. yucs@fcu.edu.tw
authors.forename: Chin-Sheng
authors.initials: CS
authors.lastname: Yu
authors.forename: Chih-Hao
authors.initials: CH
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D000818:Animals; D021301:Antifreeze Proteins / Q000737:chemistry* / Q000378:metabolism*; D019295:Computational Biology; D029941:Fish Proteins / Q000737:chemistry / Q000378:metabolism; D019476:Insect Proteins / Q000737:chemistry / Q000378:metabolism; D008958:Models, Molecular
chemical_list: D021301:Antifreeze Proteins; D029941:Fish Proteins; D019476:Insect Proteins