Aplicação de Evolução Diferencial em GPU Para o Problema de Predição de Estrutura de Proteínas com Modelo 3D AB Off-Lattice
Resumo
A função que uma proteína exerce está diretamente relacionada com a sua estrutura tridimensional. Porém, para a maior parte das proteínas atualmente sequenciadas ainda não se conhece sua forma estrutural nativa. Este artigo propõe a utilização do algoritmo de Evolução Diferencial (DE) desenvolvido na plataforma NVIDIA CUDA aplicado ao modelo 3D AB Off-Lattice para Predição de Estrutura de Proteínas. Uma estratégia de nichos e crowding foi implementada no algoritmo DE combinada com técnicas de autoajuste de parâmetros, rotinas para reinicialização da população, dois níveis de otimização e busca local. Quatro proteínas reais foram utilizadas para experimentação e os resultados obtidos se mostram competitivos com o estado-da-arte. A utilização de paralelismo massivo através da GPU ressalta a aplicabilidade desses recursos a esta classe de problemas atingindo acelerações de 708.78x para a maior cadeia proteica.Referências
Alberts B, Johnson A, L. J. e. a. (2002). Molecular Biology of the Cell. Garland Science, 4th edition edition.
Boiani, M. and Parpinelli, R. S. (2020). A GPU-based hybrid jDE algorithm applied to the 3d-AB protein structure prediction. Swarm and Evolutionary Computation, 58:100711.
Boškoviíc, B. and Brest, J. (2016). Differential evolution for protein folding optimization based on a three-dimensional AB off-lattice model. Journal of Molecular Modeling, 22(10).
Boškoviíc, B. and Brest, J. (2018). Protein folding optimization using differential evolution extended with local search and component reinitialization. Information Sciences, 454-455:178–199.
Boškoviíc, B. and Brest, J. (2019). Two-level protein folding optimization on a threedimensional ab off-lattice model.
Deng, L., Zhang, L., Sun, H., and Qiao, L. (2019). DSM-DE: a differential evolution with dynamic speciation-based mutation for single-objective optimization. Memetic Computing, 12(1):73–86.
Fraenkel, A. (1993). Complexity of protein folding. Bulletin of Mathematical Biology, 55(6):1199–1210.
Jana, N. D., D. S. and Sil, J. (2018). A Metaheuristic Approach to Protein Structure Prediction. Springer International Publishing.
Li, B., Chiong, R., and Lin, M. (2015). A balance-evolution articial bee colony algorithm for protein structure optimization based on a three-dimensional AB off-lattice model. Computational Biology and Chemistry, 54:1–12.
Li, T., Zhou, C., Wang, B., Xiao, B., and Zheng, X. (2018). A hybrid algorithm based on articial bee colony and pigeon inspired optimization for 3d protein structure prediction. Journal of Bionanoscience, 12(1):100–108.
Opara, K. R. and Arabas, J. (2019). Differential evolution: A survey of theoretical analyses. Swarm and Evolutionary Computation, 44:546 – 558.
Parpinelli, R., Felippe Plichoski, G., Silva, R., and Narloch, P. (2019). A review of techniques for online control of parameters in swarm intelligence and evolutionary computation algorithms. International Journal of Bio-Inspired Computation, 13:1–20.
Siarry, P., editor (2016). Metaheuristics. Springer International Publishing.
Soyata, T. (2018). GPU Parallel Program Development Using CUDA.
Stillinger, F. H., Head-Gordon, T., and Hirshfeld, C. L. (1993). Toy model for protein folding. Physical Review E, 48(2):1469–1477.
Boiani, M. and Parpinelli, R. S. (2020). A GPU-based hybrid jDE algorithm applied to the 3d-AB protein structure prediction. Swarm and Evolutionary Computation, 58:100711.
Boškoviíc, B. and Brest, J. (2016). Differential evolution for protein folding optimization based on a three-dimensional AB off-lattice model. Journal of Molecular Modeling, 22(10).
Boškoviíc, B. and Brest, J. (2018). Protein folding optimization using differential evolution extended with local search and component reinitialization. Information Sciences, 454-455:178–199.
Boškoviíc, B. and Brest, J. (2019). Two-level protein folding optimization on a threedimensional ab off-lattice model.
Deng, L., Zhang, L., Sun, H., and Qiao, L. (2019). DSM-DE: a differential evolution with dynamic speciation-based mutation for single-objective optimization. Memetic Computing, 12(1):73–86.
Fraenkel, A. (1993). Complexity of protein folding. Bulletin of Mathematical Biology, 55(6):1199–1210.
Jana, N. D., D. S. and Sil, J. (2018). A Metaheuristic Approach to Protein Structure Prediction. Springer International Publishing.
Li, B., Chiong, R., and Lin, M. (2015). A balance-evolution articial bee colony algorithm for protein structure optimization based on a three-dimensional AB off-lattice model. Computational Biology and Chemistry, 54:1–12.
Li, T., Zhou, C., Wang, B., Xiao, B., and Zheng, X. (2018). A hybrid algorithm based on articial bee colony and pigeon inspired optimization for 3d protein structure prediction. Journal of Bionanoscience, 12(1):100–108.
Opara, K. R. and Arabas, J. (2019). Differential evolution: A survey of theoretical analyses. Swarm and Evolutionary Computation, 44:546 – 558.
Parpinelli, R., Felippe Plichoski, G., Silva, R., and Narloch, P. (2019). A review of techniques for online control of parameters in swarm intelligence and evolutionary computation algorithms. International Journal of Bio-Inspired Computation, 13:1–20.
Siarry, P., editor (2016). Metaheuristics. Springer International Publishing.
Soyata, T. (2018). GPU Parallel Program Development Using CUDA.
Stillinger, F. H., Head-Gordon, T., and Hirshfeld, C. L. (1993). Toy model for protein folding. Physical Review E, 48(2):1469–1477.
Publicado
21/10/2020
Como Citar
DIAS, André; BOIANI, Mateus; PARPINELLI, Rafael.
Aplicação de Evolução Diferencial em GPU Para o Problema de Predição de Estrutura de Proteínas com Modelo 3D AB Off-Lattice. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 21. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
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p. 323-334.
DOI: https://doi.org/10.5753/wscad.2020.14080.
