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A Biased Random Key Genetic Algorithm with Local Search Chains for Molecular Docking

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Book cover Applications of Evolutionary Computation (EvoApplications 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11454))

Abstract

Molecular Docking is an essential tool in drug discovery. The procedure for finding the best energy affinity between ligand-receptor molecules is a computationally expensive optimization process because of the roughness of the search space and the thousands of possible conformations of ligand. In this way, besides a realistic energy function to evaluate possible solutions, a robust search method must be applied to avoid local minimums. Recently, many algorithms have been proposed to solve the docking problem, mainly based on Evolutionary Strategies. However, the question remained unsolved and its needed the development of new and efficient techniques. In this paper, we developed a Biased Random Key Genetic Algorithm, as global search procedure, hybridized with three variations of Hill-climbing and a Simulated Annealing version, as local search strategies. To evaluate the receptor-ligand binding affinity we used the Rosetta scoring function. The proposed approaches have been tested on a benchmark of protein-ligand complexes and compared to existing tools AUTODOCK VINA, DOCKTHOR, and jMETAL. A statistical test was performed on the results, and shown that the application of local search methods provides better solutions for the molecular docking problem.

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References

  1. López-Camacho, E., Godoy, M.J.G., Nebro, A.J., Aldana-Montes, J.F.: jMetalCpp: optimizing molecular docking problems with a C++ metaheuristic framework. Bioinformatics 20, 437–438 (2013)

    Google Scholar 

  2. García-Godoy, M.J., López-Camacho, E., García-Nieto, J., Nebro, A.J., Aldana-Montes, J.F.: Solving molecular docking problems with multi-objective metaheuristics. Molecules 20(6), 10154–10183 (2015)

    Article  Google Scholar 

  3. Stockwell, G.R., Thornton, J.M.: Conformational diversity of ligands bound to proteins. J. Mol. Biol. 356(4), 928–944 (2006)

    Article  Google Scholar 

  4. Sadjad, B., Zsoldos, Z.: Toward a robust search method for the protein-drug docking problem. IEEE/ACM Trans. Comput. Biol. Bioinf. 8, 1120–1133 (2011)

    Article  Google Scholar 

  5. Brooijmans, N., Kuntz, I.D.: Molecular recognition and docking algorithms. Annu. Rev. Biophys. Biomol. Struct. 32(1), 335–373 (2003)

    Article  Google Scholar 

  6. Kukkonen, S., Lampinen, J.: GDE3: the third evolution step of generalized differential evolution. In: IEEE CEC, pp. 443–450 (2005)

    Google Scholar 

  7. Nebro, A., Durillo, J., García-Nieto, J., Coello, C., Luna, F., Alba, E.: SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: 2009 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, pp. 66–73 (2009)

    Google Scholar 

  8. Gonçalves, J.F., Resende, M.G.C.: Biased random-key genetic algorithms for combinatorial optimization. J. Heuristics 17(5), 487–525 (2011)

    Article  Google Scholar 

  9. Leonhart, P.F., Spieler, E., Ligabue-Braun, R., Dorn, M.: A biased random key genetic algorithm for the protein–ligand docking problem. Soft Comput. 1–22 (2018)

    Google Scholar 

  10. Gray, J.J., et al.: Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. J. Mol. Biol. 331(1), 281–299 (2003)

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Chaudhury, S., Lyskov, S., Gray, J.J.: PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta. Bioinformatics 26, 689–691 (2010)

    Article  Google Scholar 

  13. Huang, S.Y., Zou, X.: Advances and challenges in protein-ligand docking. Int. J. Mol. Sci. 11(8), 3016 (2010)

    Article  Google Scholar 

  14. Lameijer, E.W., Back, T., Kok, J.N., Ijzerman, A.D.P.: Evolutionary algorithms in drug design. Nat. Comput. 4, 177–243 (2005)

    Article  MathSciNet  Google Scholar 

  15. Rosin, C.D., Halliday, R.S., Hart, W.E., Belew, R.K.: A comparison of global and local search methods in drug docking. In: Proceedings of the Seventh International Conference on Genetic Algorithms, pp. 221–228. Morgan Kaufmann (1997)

    Google Scholar 

  16. Ruiz-Tagle, B., Villalobos-Cid, M., Dorn, M., Inostroza-Ponta, M.: Evaluating the use of local search strategies for a memetic algorithm for the protein-ligand docking problem. In: 2017 36th International Conference of the Chilean Computer Science Society (SCCC), pp. 1–12, October 2017

    Google Scholar 

  17. Halperin, I., Ma, B., Wolfson, H., Nussinov, R.: Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins Struct. Funct. Bioinf. 47(4), 409–443 (2002)

    Article  Google Scholar 

  18. Taylor, R., Jewsbury, P., Essex, J.: A review of protein-small molecule docking methods. J. Comput. Aided Mol. Des. 16(3), 151–166 (2002)

    Article  Google Scholar 

  19. Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Courier Corporation, North Chelmsford (1998)

    MATH  Google Scholar 

  20. Aarts, E., Lenstra, J.K. (eds.): Local Search in Combinatorial Optimization, 1st edn. Wiley, New York (1997)

    MATH  Google Scholar 

  21. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  Google Scholar 

  22. Černý, V.: Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J. Optim. Theory Appl. 45(1), 41–51 (1985)

    Article  MathSciNet  Google Scholar 

  23. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  24. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1992)

    Book  Google Scholar 

  25. Gonçalves, J.F., de Almeida, J.R.: A hybrid genetic algorithm for assembly line balancing. J. Heuristics 8(6), 629–642 (2002)

    Article  Google Scholar 

  26. Goulart, N., de Souza, S.R., Dias, L.G.S., Noronha, T.F.: Biased random-key genetic algorithm for fiber installation in optical network optimization. In: 2011 IEEE Congress of Evolutionary Computation (CEC), pp. 2267–2271, June 2011

    Google Scholar 

  27. Molina, D., Lozano, M., Herrera, F.: Memetic algorithm with local search chaining for continuous optimization problems: a scalability test. In: ISDA 2009–9th International Conference on Intelligent Systems Design and Applications, pp. 1068–1073 (2009)

    Google Scholar 

  28. Molina, D., Lozano, M., Sánchez, A.M., Herrera, F.: Memetic algorithms based on local search chains for large scale continuous optimisation problems: MA-SSW-Chains. Soft Comput. 15(11), 2201–2220 (2011)

    Article  Google Scholar 

  29. Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., Olson, A.J.: Autodock4 and autodocktools4: automated docking with selective receptor flexibility. J. Comput. Chem. 30(16), 2785–2791 (2009)

    Article  Google Scholar 

  30. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The protein data bank. Nucleic Acids Res. 28(1), 235–242 (2000)

    Article  Google Scholar 

  31. Schrödinger, LLC: The AxPyMOL molecular graphics plugin for Microsoft PowerPoint, version 1.8, November 2015

    Google Scholar 

  32. O’Boyle, N.M., Banck, M., James, C.A., Morley, C., Vandermeersch, T., Hutchison, G.R.: Open babel: an open chemical toolbox. J. Cheminf. 3(1), 1–14 (2011)

    Article  Google Scholar 

  33. Trott, O., Olson, A.J.: AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31(2), 455–461 (2010)

    Google Scholar 

  34. de Magalhães, C.S., Almeida, D.M., Barbosa, H.J.C., Dardenne, L.E.: A dynamic niching genetic algorithm strategy for docking highly flexible ligands. Inf. Sci. 289, 206–224 (2014)

    Article  Google Scholar 

  35. Durillo, J., Nebro, A.: jMetal: a Java framework for multi-objective optimization. Adv. Eng. Softw. 42, 760–771 (2011)

    Article  Google Scholar 

  36. Dunn, O.J.: Multiple comparisons using rank sums. Technometrics 6(3), 241–252 (1964)

    Article  Google Scholar 

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Acknowledgements

This work was supported by grants from FAPERGS [16/2551-0000520-6], MCT/CNPq [311022/2015-4; 311611/2018-4], CAPES-STIC AMSUD [88887.135130/2017-01] - Brazil, Alexander von Humboldt-Stiftung (AvH) [BRA 1190826 HFST CAPES-P] - Germany. This study was financed in part by the Coordenacão de Aperfei çoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001.

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Authors and Affiliations

  1. Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Pablo F. Leonhart & Márcio Dorn

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  1. Pablo F. Leonhart
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  2. Márcio Dorn
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Correspondence to Márcio Dorn .

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Editors and Affiliations

  1. University of Mainz, Mainz, Germany

    Paul Kaufmann

  2. University of Granada, Granada, Spain

    Pedro A. Castillo

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Leonhart, P.F., Dorn, M. (2019). A Biased Random Key Genetic Algorithm with Local Search Chains for Molecular Docking. In: Kaufmann, P., Castillo, P. (eds) Applications of Evolutionary Computation. EvoApplications 2019. Lecture Notes in Computer Science(), vol 11454. Springer, Cham. https://doi.org/10.1007/978-3-030-16692-2_24

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  • DOI: https://doi.org/10.1007/978-3-030-16692-2_24

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