A parallel genetic algorithm for protein folding prediction using the 3D-HP side chain model

Authors:
César Manuel Vargas Benítez;Heitor Silvério Lopes
Affiliations:
Bioinformatics Laboratory, Federal University of Technology, Paraná, Curitiba, PR, Brazil;Bioinformatics Laboratory, Federal University of Technology, Paraná, Curitiba, PR, Brazil
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 9
Cited 2

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator

ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Parallel Processing and Parallel Algorithms: Theory and Computation

Parallel Processing and Parallel Algorithms: Theory and Computation
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
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
Parallel Metaheuristics: A New Class of Algorithms

Parallel Metaheuristics: A New Class of Algorithms
Reconfigurable computing for accelerating protein folding simulations

ARC'07 Proceedings of the 3rd international conference on Reconfigurable computing: architectures, tools and applications
Self-adapting evolutionary parameters: encoding aspects for combinatorial optimization problems

EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization

Protein structure prediction in lattice models with particle swarm optimization

ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Parallelism, hybridism and coevolution in a multi-level ABC-GA approach for the protein structure prediction problem

Concurrency and Computation: Practice & Experience

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work presents a methodology for the application of a parallel genetic algorithm (PGA) to the problem of protein folding prediction, using the 3DHP-Side Chain model. This model is more realistic than the usual 3DHP model but, on the other hand, it is has a higher degree of complexity. Specific encoding and fitness function were proposed for this model, and running parameters were experimentally set for the standard master-slave PGA. The system was tested with a benchmark of synthetic sequences, obtaining good results. An analysis of performance of the parallel implementation was done, compared with the sequential version. Overall results suggest that the approach is efficient and promising.