A biased random key genetic algorithm for the protein–ligand docking problem
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Molecular docking is a valuable tool for drug discovery. Receptor and flexible Ligand docking is a very computationally expensive process due to a large number of degrees of freedom of the ligand and the roughness of the molecular binding search space. A molecular docking simulation starts with receptor and ligand unbound structures, and the algorithm tests hundreds of thousands of ligand conformations and orientations to find the best receptor–ligand binding affinity by assigning and optimizing an energy function. Although the advances in the conception of methods and computational strategies for searching the best protein–ligand binding affinity, the development of new strategies, the adaptation, and investigation of new approaches and the combination of existing and state-of-the-art computational methods and techniques to the molecular docking problem are needed. We developed a Biased Random Key Genetic Algorithm as a sampling strategy to search the protein–ligand conformational space. We use a different method to discretize the search space. The proposed method (namely, BRKGA-DOCK) has been tested on a selection of protein–ligand complexes and compared to existing tools AUTODOCK VINA, DOCKTHOR, and a multiobjective approach (jMETAL). Compared to other traditional docking software, the proposed method shows best average Root-Mean-Square Deviation. Structural results were also statistically analyzed. The proposed method proved to be efficient and a good alternative for the molecular docking problem.
KeywordsMolecular docking Optimization BRKGA Nature-inspired metaheuristics
This study was funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (Grant Number 473692/2013-9 and 311022/2015-4); the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES); the Alexander von Humboldt-Foundation; and the Fundação de Amparo a Pesquisa do Estado do Rio Grande do Sul (FAPERGS) (grant PRONUPEQ). This Research was supported by Microsoft under a Microsoft Azure for Research Award. We thank Dr. Mathias Krause (KIT, Germany) for helpful discussions.
Compliance with Ethical Standards
Conflicts of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Abdi H (2007) Bonferroni and sidak corrections for multiple comparisons. In: Salkind NJ (ed) Encyclopedia of measurement and statistics. Sage, Thousand Oaks, pp 103–107Google Scholar
- Atilgan E, Hu J (2010) Efficient protein-ligand docking using sustainable evolutionary algorithms. In: 2010 10th international conference on hybrid intelligent systems (HIS), pp 113–118Google Scholar
- Fu Y, Wu X, Chen Z, Sun J, Zhao J, Xu W (2015) A new approach for flexible molecular docking based on swarm intelligence. Math Probl Eng 2015. https://doi.org/10.1155/2015/540186
- Goulart N, Souza SR, Dias LGS, Noronha TF (2011) Biased random-key genetic algorithm for fiber installation in optical network optimization. In: 2011 IEEE CEC, pp 2267–2271Google Scholar
- Kang L, Wang X (2012) Multi-scale optimization model and algorithm for computer-aided molecular docking. In: 2012 eighth international conference on natural computation (ICNC), pp 1208–1211Google Scholar
- Kukkonen S, Lampinen J (2005) GDE3: The third evolution step of generalized differential evolution. In: IEEE congress on evolutionary computation, pp 443–450Google Scholar
- Lopez-Camacho E, Godoy MJG, Nebro AJ, Aldana-Montes JF (2013) jMetalCpp: optimizing molecular docking problems with a C++ metaheuristic framework. Bioinformatics 30(3):437–438. https://doi.org/10.1093/bioinformatics/btt679
- Marchiori E, Moore JH, Rajapakse JC (2007) Evolutionary computation, machine learning and data mining in bioinformatics. In: Proceedings 5th European conference, EvoBIO 2007, Valencia, Spain, April 11–13, 2007, vol 4447Google Scholar
- Morris GM, Goodsell DS, Pique ME, Lindstrom W, Huey R, Forli S, Hart WE, Halliday S, Belew R, Olson AJ (2011) Autodock 4.2 user guide: automated docking of flexible ligands to flexible receptors. The scripps research institute. http://autodock.scripps.edu/faqs-help/manual/autodock-4-2-user-guide. Accessed Jan 2018
- Nebro AJ, Durillo JJ, García-Nieto J, Coello CA, Luna F, Alba E (2009) Smpso: a new pso-based metaheuristic for multi-objective optimization. In: 2009 IEEE symposium on computational intelligence in multicriteria decision-making, pp 66–73Google Scholar
- Peter N (1963) Distribution-free multiple comparisons. Princeton University, PrincetonGoogle Scholar
- Prasetyo H, Amer Y, Fauza G, Lee SH (2015) Survey on applications of biased-random key genetic algorithms for solving optimization problems. In: Ind. Eng. and Eng. Manag. (IEEM), pp 863–870Google Scholar
- Schrödinger LLC (2015) The PyMOL molecular graphics system, version 1.8. Schrödinger LLC, New YorkGoogle Scholar
- Silva RMA, Resende MGC, Pardalos PM, Fac JL (2013) Biased random-key genetic algorithm for nonlinearly-constrained global optimization. In: 2013 IEEE CEC, pp 2201–2206Google Scholar
- Trott O, Olson AJ (2010) Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461Google Scholar
- Wilcoxon F (1992) Individual comparisons by ranking methods. Springer, New York, pp 196–202Google Scholar
- Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver Press, BeckingtonGoogle Scholar