Enhanced Genetic Algorithm for Protein Structure Prediction based on the HP Model

Mansour, Nashat; Kanj, Fatima; Khachfe, Hassan

doi:10.5772/15796

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…Also, in the work of Mansour et al [19], their GA was enhanced with heuristics that repair infeasible outcomes of the crossover operation and ensure that the mutation operation leads to fitter and feasible candidate solutions, obtaining again a decrease of necessary evaluations over the previous works (the authors do not specify how many runs were applied for each sequence).…”

Section: Resultsmentioning

confidence: 99%

“…The better values (in terms of number of evaluations) in some sequences in [19] are explained because of the filters applied by the authors to the outcome of the crossover and mutation operators in their enhanced GA.…”

Section: Resultsmentioning

confidence: 99%

“…Different temporal steps in the folding process with protein sequence HPPHPPHPPHPPHPPHP-PHPPH.Seq.neural-CA (2) neural-CA (1) M et al GA[19] J&K GA[13] P et al GA[20] U&M GA […”

mentioning

confidence: 99%

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Protein folding with cellular automata in the 3D HP model

Santos

Villot

Diéguez

2013

Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation

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In the difficult ab initio prediction in protein folding only the information of the primary structure of amino acids is used to determine the final folded conformation. The complexity of the interactions and the nature of the amino acid elements are reduced with the use of lattice models like HP, which categorizes the amino acids regarding their hydrophobicity. On the contrary to the intense research performed on the direct prediction of the final folded conformation, our aim here is to model the dynamic and emergent folding process through time, using the scheme of cellular automata but implemented with artificial neural networks optimized with Differential Evolution. Moreover, as the iterative folding also provides the final folded conformation, we can compare the results with those from direct prediction methods of the final protein conformation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Protein folding with cellular automata in the 3D HP model

Santos

Villot

Diéguez

2013

Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation

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“…To summarize, although more works should be done in this direction, in the last years the parallelization of MOEAs is getting more attention and several works are including it as their future works [182,183].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Soft Computing Techniques for the Protein Folding Problem on High Performance Computing Architectures

Llanes¹,

Muñoz²,

Bueno-Crespo³

et al. 2016

CDT

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The protein-folding problem has been extensively studied during the last fifty years. The understanding of the dynamics of global shape of a protein and the influence on its biological function can help us to discover new and more effective drugs to deal with diseases of pharmacological relevance. Different computational approaches have been developed by different researchers in order to foresee the threedimensional arrangement of atoms of proteins from their sequences. However, the computational complexity of this problem makes mandatory the search for new models, novel algorithmic strategies and hardware platforms that provide solutions in a reasonable time frame. We present in this revision work the past and last tendencies regarding protein folding simulations from both perspectives; hardware and software. Of particular interest to us are both the use of inexact solutions to this computationally hard problem as well as which hardware platforms have been used for running this kind of Soft Computing techniques.

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“…Computational methods use different algorithms such as Evolutionary Algorithm (EA) and Genetic Algorithm (GA) [3,5,6,[12][13][14][15], Particle Swarm Optimization (PSO) [3,8], Ant Colony Optimization (ACO) [16][17][18], Honey Bee Algorithm [7,10,[19][20][21] and etc. .…”

Section: Introductionmentioning

confidence: 99%

Protein structure prediction using bio-inspired algorithm: A review

Karami

Khakzad

Shirazi

et al. 2012

The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012)

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One of the main problems in Bioinformatics is the prediction of protein tertiary structure from its amino acid sequence. This problem is computationally complex and belongs to the NP-hard class. Although these years many algorithms and methods have been introduced, this problem is still an open area for research. In this paper we discuss the most important algorithms like evolutionary algorithms, particle swarm or ant colony optimization and computational methods introduced in this field and their challenges.

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Enhanced Genetic Algorithm for Protein Structure Prediction based on the HP Model

Cited by 6 publications

References 30 publications

Protein folding with cellular automata in the 3D HP model

Protein folding with cellular automata in the 3D HP model

Soft Computing Techniques for the Protein Folding Problem on High Performance Computing Architectures

Protein structure prediction using bio-inspired algorithm: A review

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