A Grid-Based Hybrid Hierarchical Genetic Algorithm for Protein Structure Prediction View Full Text


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Chapter Info

DATE

2010

AUTHORS

Alexandru-Adrian Tantar , Nouredine Melab , El-Ghazali Talbi

ABSTRACT

A hybrid hierarchical conformational sampling evolutionary algorithm is presented in this chapter, relying on different parallelization models. After first reviewing general conformational sampling aspects, e.g. existing approaches, complexity matters, force field functions, a focus is considered for the protein structure prediction problem. Furthermore, having as basis the highly multimodal nature of the energy landscape structure, a hybrid evolutionary approach is defined, enclosing conjugate gradient and adaptive simulated annealing enforced components. An insular model is employed, the conformational sampling process being conducted on a collaborative basis. Nonetheless, although low energy conformations were obtained, no close to native conformations were attained. Consequently, a higher complexity hierarchical paradigm has been constructed, with incentive following results. More... »

PAGES

291-319

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-10675-0_13

DOI

http://dx.doi.org/10.1007/978-3-642-10675-0_13

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023373102


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