A biased random key genetic algorithm for the protein–ligand docking problem
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.
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