An enhanced genetic algorithm for protein structure prediction using the 2d hydrophobic-polar model

Authors:
Heitor S. Lopes;Marcos P. Scapin
Affiliations:
Bioinformatics Lab. (CPGEI), Centro Federal de Educação Tecnológica do Paraná, Curitiba, Brazil;Bioinformatics Lab. (CPGEI), Centro Federal de Educação Tecnológica do Paraná, Curitiba, Brazil
Venue:
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Year:
2005

Citing 3
Cited 6

Genetic Algorithm for 3D Protein Folding Simulations

Proceedings of the 5th International Conference on Genetic Algorithms
A Standard GA Approach to Native Protein Conformation Prediction

Proceedings of the 6th International Conference on Genetic Algorithms
Protein Structure Prediction by Applying an Evolutionary Algorithm

IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing

Novel Memetic Algorithm for Protein Structure Prediction

AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Reconfigurable computing for accelerating protein folding simulations

ARC'07 Proceedings of the 3rd international conference on Reconfigurable computing: architectures, tools and applications
Differential evolution for protein structure prediction using the HP model

IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
A memetic approach to protein structure prediction in triangular lattices

ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Parallelism, hybridism and coevolution in a multi-level ABC-GA approach for the protein structure prediction problem

Concurrency and Computation: Practice & Experience
Hybrid evolutionary algorithm with a composite fitness function for protein structure prediction

IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an enhanced genetic algorithm for the protein structure prediction problem. A new fitness function, that uses the concept of radius of gyration, is proposed. Also, a novel operator called partial optimization, together with different strategies for performance improvement, are described. Tests were done with five different amino acid chains from 20 to 85 residues long and better results were obtained, when compared with those in the current literature. Results are promising and suggest the suitability of the proposed method for protein structure prediction using the 2D HP model. Further experiments shall be done with longer amino acid chains as well as with real-world proteins.