Solving Protein Folding Problem using Elitism-Based Compact Genetic Algorithm

Abstract

Proteins are vital components of living cells. A number of diseases such as Alzheimer's, Cystic fibrosis and Mad Cow diseases are shown to result from misfunctioning of proteins. Problem statement: Protein folding problem is the process of predicting the optimal 3D molecular structure of a protein, or tertiary structure, which is an indication of its proper function. Approach: An enhancement over persistent elitist compact genetic algorithm (pe-cGA) was made to minimize the energy of proteins indicating how far it is from its optimal 3D structure. Energy was calculated using the Empirical Conformational Energy Program for Peptides (ECEPP) package. Results: Experiments were performed on the Met-enkephalin protein. The enhanced algorithm reached an energy of -7.378 in 140,000 iterations surpassing the Distributed Genetic Algorithm (DGA) which reached the same energy in 700,000 iterations. A comparison was also made with the Breeder Genetic Algorithm (BGA) which did not reach this energy in the first place. Conclusions/Recommendations: Results show that the enhanced algorithm is superior to DGA and BGA and a computational alternative to costly laboratory methods and an efficient means for solving organic docking problems.