title: Optimal subset selection of primary sequence features using the genetic algorithm for thermophilic proteins identification.
abstract: A genetic algorithm (GA) coupled with multiple linear regression (MLR) was used to extract useful features from amino acids and g-gap dipeptides for distinguishing between thermophilic and non-thermophilic proteins. The method was trained by a benchmark dataset of 915 thermophilic and 793 non-thermophilic proteins. The method reached an overall accuracy of 95.4 % in a Jackknife test using nine amino acids, 38 0-gap dipeptides and 29 1-gap dipeptides. The accuracy as a function of protein size ranged between 85.8 and 96.9 %. The overall accuracies of three independent tests were 93, 93.4 and 91.8 %. The observed results of detecting thermophilic proteins suggest that the GA-MLR approach described herein should be a powerful method for selecting features that describe thermostabile machines and be an aid in the design of more stable proteins.
authors.affiliation: Department of Biochemistry and Molecular Biology, College of Life Science, Nankai University, Weijin Road 94, Tianjin, 300071, China, wlqg1983@163.com.
authors.forename: LiQiang
authors.initials: L
authors.lastname: Wang
authors.forename: CuiFeng
authors.initials: C
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D030562:Databases, Protein; D004151:Dipeptides / Q000737:chemistry; D016014:Linear Models; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D020539:Sequence Analysis, Protein; D013696:Temperature
chemical_list: D000596:Amino Acids; D004151:Dipeptides; D011506:Proteins