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A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction

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Abstract

A hierarchical hybrid model of parallel metaheuristics is proposed, combining an evolutionary algorithm and an adaptive simulated annealing. The algorithms are executed inside a grid environment with different parallelization strategies: the synchronous multi-start model, parallel evaluation of different solutions and an insular model with asynchronous migrations. Furthermore, a conjugated gradient local search method is employed at different stages of the exploration process. The algorithms were evaluated using the protein structure prediction problem, having as benchmarks the tryptophan-cage protein (Brookhaven Protein Data Bank ID: 1L2Y), the tryptophan-zipper protein (PDB ID: 1LE1) and the α-Cyclodextrin complex. Experimentations were performed on a nation-wide grid infrastructure, over six distinct administrative domains and gathering nearly 1,000 CPUs. The complexity of the protein structure prediction problem remains prohibitive as far as large proteins are concerned, making the use of parallel computing on the computational grid essential for its efficient resolution.

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Authors and Affiliations

  1. Laboratoire d’Informatique Fondamentale de Lille, LIFL/CNRS UMR 8022, DOLPHIN Project - INRIA Futurs, Cité Scientifique, 59655, Villeneuve d’Ascq Cedex, France

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

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  1. Alexandru-Adrian Tantar
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  2. Nouredine Melab
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  3. El-Ghazali Talbi
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Correspondence to Alexandru-Adrian Tantar.

Additional information

The current article is developed within the context of the DOCK—Conformational Sampling and Docking on Grids project, sustained by ANR (Agence Nationale de la Recherche, http://www.gip-anr.fr). The project is a joint work between LIFL (USTL-CNRS-INRIA), IBL (CNRS-INSERM) and CEA DSV/DRDC.

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Tantar, AA., Melab, N. & Talbi, EG. A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction. Soft Comput 12, 1185–1198 (2008). https://doi.org/10.1007/s00500-008-0298-8

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