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Title

Solving Protein Folding Problem Using Hybrid Genetic Clonal Selection Algorithm

Author

Adel Omar Mohamed, Abdelfatah A. Hegazy, Amr Badr

Citation

Vol. 10  No. 12  pp. 94-98

Abstract

Alzheimer’s disease, Cystic fibrosis, Mad Cow disease, an inherited form of emphysema, even many cancers. Recent discoveries show that all these apparently unrelated diseases result from protein folding gone wrong. As though that weren’t enough, many of the unexpected difficulties biotechnology companies encounter when trying to produce human proteins in bacteria also result from something amiss when proteins fold. 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 clonal selection algorithm was made to minimize the energy of proteins by adding crossover function from Genetic algorithm (GA). 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 energy of -20.919 in 10 generations surpassing the Clonal Selection Algorithm which reached the same energy in 30 generations. A comparison was also made with the Genetic Algorithm (GA) which reaches this energy in 1000 generations. Results show that the enhanced algorithm is superior to Clonal Selection algorithm and GA.

Keywords

Protein Folding, genetic algorithms, artificial immune system, Clonal Selection Algorithm, Met-Enkephalin

URL

http://paper.ijcsns.org/07_book/201012/20101214.pdf