Solving molecular flexible docking problems with metaheuristics: A comparative study
The main objective of the molecular docking problem is to find a conformation between a small molecule (ligand) and a receptor molecule with minimum binding energy. The quality of the docking score depends on two factors: the scoring function and the search method being used to find the lowest bindi...
| Autores: | , , , , |
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| Formato: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2015 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/109028 |
| Acesso em linha: | https://hdl.handle.net/11441/109028 https://doi.org/10.1016/j.asoc.2014.10.049 |
| Access Level: | acceso abierto |
| Palavra-chave: | Molecular Docking Optimization Metaheuristics Experimental comparison |
| Resumo: | The main objective of the molecular docking problem is to find a conformation between a small molecule (ligand) and a receptor molecule with minimum binding energy. The quality of the docking score depends on two factors: the scoring function and the search method being used to find the lowest binding energy solution. In this context, AutoDock 4.2 is a popular C++ software package in the bioinformatics community providing both elements, including two genetic algorithms, one of them endowed with a local search strategy. This paper principally focuses on the search techniques for solving the docking problem. In using the AutoDock 4.2 scoring function, the approach in this study is twofold. On the one hand, a number of four metaheuristic techniques are analyzed within an extensive set of docking problems, looking for the best technique according to the quality of the binding energy solutions. These techniques are thoroughly evaluated and also compared with popular well-known docking algorithms in AutoDock 4.2. The metaheuristics selected are: generational and a steady-state Genetic Algorithm, Differential Evolution, and Particle Swarm Optimization. On the other hand, a C++ version of the jMetal optimization framework has been integrated inside AutoDock 4.2, so that all the algorithms included in jMetal are readily available to solve docking problems. The experiments reveal that Differential Evolution obtains the best overall results, even outperforming other existing algorithms specifically designed for molecular docking. |
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