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Protein structure prediction with the 3D-HP side-chain model using a master–slave parallel genetic algorithm

Abstract

This work presents a master-slave parallel genetic algorithm for the protein folding problem, using the 3D-HP side-chain model (3D-HP-SC). This model is sparsely studied in the literature, although more expressive than other lattice models. The fitness function proposed includes information not only about the free-energy of the conformation, but also compactness of the side-chains. Since there is no benchmark available to date for this model, a set of 15 sequences was used, based on a simpler model. Results show that the parallel GA achieved a good level of efficiency and obtained biologically coherent results, suggesting the adequacy of the methodology. Future work will include new biologically-inspired genetic operators and more experiments to create new benchmarks.

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Correspondence to César Manuel Vargas Benítez.

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This paper is an extended version of a paper that appeared at ENIA 2009 (The Brazilian Meeting on Artificial Intelligence).

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Benítez, C.M.V., Lopes, H.S. Protein structure prediction with the 3D-HP side-chain model using a master–slave parallel genetic algorithm. J Braz Comput Soc 16, 69–78 (2010). https://doi.org/10.1007/s13173-010-0002-6

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Keywords

  • Genetic algorithm
  • Bioinformatics
  • Protein folding
  • 3D-HP-SC