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.