Identifying peptide motifs using genetic algorithms

Abstract

Finding the ligand motifs binding to the receptor molecules is crucial in vaccine and drug design, especially for the MHC-peptide problem. In this work, for determining the peptide motifs binding to specific MHC molecules, we have used regression analysis. In order to find the the optimum regression line, genetic algorithm (GA) techniques are used because in traditional regression analysis methods, you may not be able to reach the optimum solution. The optimum regression line generated by the GA also determines the factors on the MHC molecules that makes the peptide bind to these MHC molecules. The efficiency of the GA is tested by doing several tests on its different parameters, and the optimum set of parameters are determined for this problem. Results have shown that we are able to predict second position of a peptide motif with 95 per cent exact match or 100 per cent close match within one standard deviation of the predicted equation. We have divided last position's data into two parts in order to explain it with two regression lines. Predictions for the last position of the peptide motif with the first regression line resulted in 80 per cent exact match. Second regression line resulted in 75 per cent exact match.