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

Authors:
Alexandru-Adrian Tantar;Nouredine Melab;El-Ghazali Talbi
Affiliations:
DOLPHIN Project - INRIA Futurs, Cité Scientifique, Laboratoire d’Informatique Fondamentale de Lille, LIFL/CNRS UMR 8022, 59655, Villeneuve d’Ascq Cedex, France;DOLPHIN Project - INRIA Futurs, Cité Scientifique, Laboratoire d’Informatique Fondamentale de Lille, LIFL/CNRS UMR 8022, 59655, Villeneuve d’Ascq Cedex, France;DOLPHIN Project - INRIA Futurs, Cité Scientifique, Laboratoire d’Informatique Fondamentale de Lille, LIFL/CNRS UMR 8022, 59655, Villeneuve d’Ascq Cedex, France
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (1143 - 1198) " Distributed Bioinspired Algorithms"; Guest editors: F. Fernández de Vega, E. Cantú-Paz
Year:
2008

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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.