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Exploiting multilevel parallelism on a many-core system for the application of hyperheuristics to a molecular docking problem

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Abstract

The solution of Protein–Ligand Docking Problems can be approached through metaheuristics, and satisfactory metaheuristics can be obtained with hyperheuristics searching in the space of metaheuristics implemented inside a parameterized schema. These hyperheuristics apply several metaheuristics, resulting in high computational costs. To reduce execution times, a shared-memory schema of hyperheuristics is used with four levels of parallelism, two for the hyperheuristic and two for the metaheuristics. The parallel schema is executed in a many-core system in “native mode,” and the four-level parallelism allows us to take full advantage of the massive parallelism offered by this architecture and obtain satisfactory fitness and an important reduction in the execution time.

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References

  1. Andrusier N, Mashiach E, Nussinov R, Wolfson HJ (2008) Principles of flexible protein-protein docking. Proteins 73(2):271–289

    Article  Google Scholar 

  2. Almeida F, Giménez D, López-Espín JJ (2011) A parameterized shared-memory scheme for parameterized metaheuristics. J Supercomput 58(3):292–301

    Article  Google Scholar 

  3. Almeida F, Giménez D, López-Espín JJ, Pérez-Pérez M (2013) Parameterised schemes of metaheuristics: basic ideas and applications with Genetic Algorithms, Scatter Search and GRASP. IEEE Trans Syst Man Cybern Part A Syst Humans 43(3):570–586

    Article  Google Scholar 

  4. Asanovic K, Bodik R, Catanzaro BC, Gebis JJ, Husbands P, Keutzer K, Patterson DA, Plishker WL, Shalf J, Williams SW, Yelick KA (2006) The landscape of parallel computing research: a view from Berkeley. Tech. rep., UCB/EECS-2006-183, EECS Department, University of California, Berkeley

  5. Burke EK, Hyde M, Kendall G, Ochoa G, Özcan E, Woodward J (2010) A classification of hyper-heuristic approaches. In: Gendreau M, Potvin J-Y (eds) Handbook of Meta-heuristics. Springer, Berlin, pp 449–468

    Google Scholar 

  6. Cutillas-Lozano J-M, Giménez D, Almeida F (2015) Hyperheuristics based on parametrized metaheuristic schemes. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 361–368

  7. Imbernón B, Cecilia JM, Giménez D (2016) Enhancing metaheuristic-based virtual screening methods on massively parallel and heterogeneous systems. In: Proceedings of the 7th International Workshop on Programming Models and Applications for Multicores and Manycores, pp 50–58

  8. Irwin JJ, Shoichet BK (2005) ZINC-a free database of commercially available compounds for virtual screening. J Chem Inf Model 45(1):177–182

    Article  Google Scholar 

  9. Jorgensen WL (2004) The many roles of computation in drug discovery. Science 303:1813–1818

    Article  Google Scholar 

  10. Navarro-Fernández J, Pérez-Sánchez H, Martínez-Martínez I, Meliciani I, Guerrero JA, Vicente V, Corral J, Wenzel W (2012) In silico discovery of a compound with nanomolar affinity to antithrombin causing partial activation and increased heparin affinity. J Med Chem 55(14):6403–6412

    Article  Google Scholar 

  11. Nobile MS, Cazzaniga P, Tangherloni A, Besozzi D (2016) Graphics processing units in bioinformatics, computational biology and systems biology. Brief Bioinform. doi:10.1093/bib/bbw058

    Google Scholar 

  12. Özcan E, Bilgin B, Korkmaz E (2008) A comprehensive analysis of hyper-heuristics. Intell Data Anal 12(1):3–23

    Google Scholar 

  13. Protein Data Bank (1971) Nature New Biol 233:223

  14. Rester U (2008) From virtuality to reality-virtual screening in lead discovery and lead optimization: a medicinal chemistry perspective. Curr Opin Drug Discov Dev 11(4):559–568

    Google Scholar 

  15. Talbi E-G, Zomaya AL (2006) Grids in bioinformatics and computational biology. J Parallel Distrib Comput 66(12):1481

    Article  Google Scholar 

  16. Vega-Rodríguez MA, González-Álvarez DL (2015) Parallelism in bioinformatics: a view from different parallelism-based technologies. Parallel Comput 42:1–3

    Article  Google Scholar 

  17. Wang J, Deng Y, Roux B (2006) Absolute binding free energy calculations using molecular dynamics simulations with restraining potentials. Biophys J 91(8):2798–2814

    Article  Google Scholar 

  18. Yang MQ, Athey BD, Arabnia HR, Sung AH, Liu Q, Yang JY, Mao J, Deng Y (2009) High-throughput next-generation sequencing technologies foster new cutting-edge computing techniques in bioinformatics. BMC Genom 10(S–1):l1

  19. Yang JY, Yang MQ, Zhu MM, Arabnia HR, Deng Y (2008) Promoting synergistic research and education in genomics and bioinformatics. BMC Genom 9(S–1):l1

  20. Yang W, Yoshigoe K, Qin X, Liu JS, Yang JY, Niemierko A, Deng Y, Liu Y, Dunker AK, Chen Z, Wang L, Xu D, Arabnia HR, Tong W, Yang MQ (2014) Identification of genes and pathways involved in kidney renal clear cell carcinoma. BMC Bioinform 15(S–17):S2

  21. Yuriev E, Agostino M, Ramsland PA (2011) Challenges and advances in computational docking: 2009 in review. J Mol Recognit 24(2):149–164

    Article  Google Scholar 

  22. Zhou Z, Felts AK, Friesner RA, Levy RM (2007) Comparative performance of several flexible docking programs and scoring functions: enrichment studies for a diverse set of pharmaceutically relevant targets. J Chem Inf Model 47(4):1599–1608

    Article  Google Scholar 

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Acknowledgements

We thank the Service of Support to Technological Research of the Technical University of Cartagena for allowing us to run some experiments in their systems and to Luis Pedro García for his guidance in using them. This work was supported by the Spanish MINECO, as well as European Commission FEDER funds, under grants TIN2015-66972-C5-3-R and TIN2016-78799-P (AEI/FEDER, UE).

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Authors and Affiliations

  1. Polytechnic School, Catholic University of San Antonio of Murcia (UCAM), Murcia, Spain

    José M. Cecilia & Baldomero Imbernón

  2. Department of Computing and Systems, University of Murcia, Murcia, Spain

    José-Matías Cutillas-Lozano & Domingo Giménez

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  1. José M. Cecilia
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  2. José-Matías Cutillas-Lozano
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  3. Domingo Giménez
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  4. Baldomero Imbernón
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Correspondence to Domingo Giménez.

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Cecilia, J.M., Cutillas-Lozano, JM., Giménez, D. et al. Exploiting multilevel parallelism on a many-core system for the application of hyperheuristics to a molecular docking problem. J Supercomput 74, 1803–1814 (2018). https://doi.org/10.1007/s11227-017-1989-7

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  • DOI: https://doi.org/10.1007/s11227-017-1989-7

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