Cellular Evolutionary Algorithms for Solving Protein Folding Problem

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 malfunctioning of proteins. 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. An enhancement over Cellular Genetic Algorithm (CGA) and Cellular Estimation of Distribution Algorithm (CEDA) 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. Experiments were performed on the Met-enkephalin protein. The enhanced algorithms reached energy of -10.1 and -10.78 for CGA and CEcGA surpassing the Elitism-based compact Genetic Algorithm and Breeder Genetic Algorithm which didn't reach this energy but reached -7.378, and Breeder Genetic Algorithm (BGA) which reached -3. Results show that the proposed two algorithms "CGA and CEDA" are better than DGA, BGA, and EcGA and a computational alternative to costly laboratory methods and an efficient means for solving organic docking problems.