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Open AccessArticle

GPathFinder: Identification of Ligand-Binding Pathways by a Multi-Objective Genetic Algorithm

Departament de Química, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Barcelona, Spain
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Int. J. Mol. Sci. 2019, 20(13), 3155; https://doi.org/10.3390/ijms20133155
Received: 31 May 2019 / Revised: 19 June 2019 / Accepted: 21 June 2019 / Published: 28 June 2019
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design)
Protein–ligand docking is a widely used method to generate solutions for the binding of a small molecule with its target in a short amount of time. However, these methods provide identification of physically sound protein–ligand complexes without a complete view of the binding process dynamics, which has been recognized to be a major discriminant in binding affinity and ligand selectivity. In this paper, a novel piece of open-source software to approach this problem is presented, called GPathFinder. It is built as an extension of the modular GaudiMM platform and is able to simulate ligand diffusion pathways at atomistic level. The method has been benchmarked on a set of 20 systems whose ligand-binding routes were studied by other computational tools or suggested from experimental “snapshots”. In all of this set, GPathFinder identifies those channels that were already reported in the literature. Interestingly, the low-energy pathways in some cases indicate novel possible binding routes. To show the usefulness of GPathFinder, the analysis of three case systems is reported. We believe that GPathFinder is a software solution with a good balance between accuracy and computational cost, and represents a step forward in extending protein–ligand docking capacities, with implications in several fields such as drug or enzyme design. View Full-Text
Keywords: multi-objective genetic algorithm; molecular modeling; ligand diffusion; computational chemistry; molecular docking; drug design multi-objective genetic algorithm; molecular modeling; ligand diffusion; computational chemistry; molecular docking; drug design
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MDPI and ACS Style

Sánchez-Aparicio, J.-E.; Sciortino, G.; Herrmannsdoerfer, D.V.; Chueca, P.O.; Pedregal, J.R.-G.; Maréchal, J.-D. GPathFinder: Identification of Ligand-Binding Pathways by a Multi-Objective Genetic Algorithm. Int. J. Mol. Sci. 2019, 20, 3155. https://doi.org/10.3390/ijms20133155

AMA Style

Sánchez-Aparicio J-E, Sciortino G, Herrmannsdoerfer DV, Chueca PO, Pedregal JR-G, Maréchal J-D. GPathFinder: Identification of Ligand-Binding Pathways by a Multi-Objective Genetic Algorithm. International Journal of Molecular Sciences. 2019; 20(13):3155. https://doi.org/10.3390/ijms20133155

Chicago/Turabian Style

Sánchez-Aparicio, José-Emilio; Sciortino, Giuseppe; Herrmannsdoerfer, Daniel V.; Chueca, Pablo O.; Pedregal, Jaime R.-G.; Maréchal, Jean-Didier. 2019. "GPathFinder: Identification of Ligand-Binding Pathways by a Multi-Objective Genetic Algorithm" Int. J. Mol. Sci. 20, no. 13: 3155. https://doi.org/10.3390/ijms20133155

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