A Multi-objective Optimization Energy Approach to Predict the Ligand Conformation in a Docking Process

Sandoval-Pérez, Angélica; Becerra, David; Vanegas, Diana; Restrepo-Montoya, Daniel; Niño, Fernando

doi:10.1007/978-3-642-37207-0_16

Cited by 10 publications

(12 citation statements)

References 22 publications

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“…Some studies have been developed over the last five years based on multi-objective optimization, such as the reviews on the multiobjective application in different fields proposed by Nicolaou et al [30] and Nicolotti et al [31]; a new hybrid algorithm proposed by Grosdidier et al [32] which uses two fitness functions where two variables are optimized; Sandoval-Perez et al [33] used the NSGA-II algorithm implemented in the jMetal framework in order to optimize two variables (bonded and non-bonded energy terms); Oduguwa et al [34] applied the NSGA-II, PAES, SPEA evolutionary multi-objective techniques optimizing up to three objectives of the energy function; and finally, Janson et al [35] applied a multiobjective optimization approach using the PSO algorithm.…”

Section: Related Workmentioning

confidence: 99%

Solving molecular flexible docking problems with metaheuristics: A comparative study

López-Camacho

García-Godoy

García-Nieto

et al. 2015

Applied Soft Computing

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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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Section: Related Workmentioning

confidence: 99%

Solving molecular flexible docking problems with metaheuristics: A comparative study

López-Camacho

García-Godoy

García-Nieto

et al. 2015

Applied Soft Computing

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“…In the same year, Boisson et al [1] implemented a parallel evolutionary bi-objective model based on optimizing two objectives: the sum of E inter and E intra and a surface term for the docking of six instances. Sandoval-Perez et al [16] used the implementation of NSGA-II provided by the jMetal framework to optimize bound and non-bound energy terms of ligand/receptor as objectives applied to four docking instances. Gu et al [6] developed a new multi-objective approach based on optimizing the solutions generated by an aggregated scoring function that includes terms from force-field, empirical and knowledge-based scoring functions.…”

Section: Introductionmentioning

confidence: 99%

A New Multi-objective Approach for Molecular Docking Based on RMSD and Binding Energy

López-Camacho

García-Godoy

García-Nieto

et al. 2016

Lecture Notes in Computer Science

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Ligand-protein docking is an optimization problem based on predicting the position of a ligand with the lowest binding energy in the active site of the receptor. Molecular docking problems are traditionally tackled with single-objective, as well as with multi-objective approaches, to minimize the binding energy. In this paper, we propose a novel multi-objective formulation that considers: the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands and the binding (intermolecular) energy, as two objectives to evaluate the quality of the ligand-protein interactions. To determine the kind of Pareto front approximations that can be obtained, we have selected a set of representative multi-objective algorithms such as NSGA-II, SMPSO, GDE3, and MOEA/D. Their performances have been assessed by applying two main quality indicators intended to measure convergence and diversity of the fronts. In addition, a comparison with LGA, a reference single-objective evolutionary algorithm for molecular docking (AutoDock) is carried out. In general, SMPSO shows the best overall results in terms of energy and and RMSD (value lower than 2Å for successful docking results). This new multi-objective approach shows an improvement over the ligand-protein docking predictions that could be promising in in silico docking studies to select new anticancer compounds for therapeutic targets that are multidrug resistant.

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“…In this sense, the use of global optimization algorithms to tackle with the molecular docking problem has been extensively studied in the past with successful results when using single-objective [3] and multi-objective methods [4,5,6,7,8]. In the case of multi-objective optimization, a series of proposals have been appearing since 2015, which involve flexibility in the side-chains of the receptor’s active site and the use of the energy scoring function provided by AutoDock (version 4.2 , The Scripps Research Institute, California, EEUU), which is one of the most used and cited tools for drug discovery.…”

Section: Introductionmentioning

confidence: 99%

Molecular Docking Optimization in the Context of Multi-Drug Resistant and Sensitive EGFR Mutants

et al. 2016

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Abstract:The human Epidermal Growth Factor (EGFR) plays an important role in signaling pathways, such as cell proliferation and migration. Mutations like G719S, L858R, T790M, G719S/T790M or T790M/L858R can alter its conformation, and, therefore, drug responses from lung cancer patients. In this context, candidate drugs are being tested and in silico studies are necessary to know how these mutations affect the ligand binding site. This problem can be tackled by using a multi-objective approach applied to the molecular docking problem. According to the literature, few studies are related to the application of multi-objective approaches by minimizing two or more objectives in drug discovery. In this study, we have used four algorithms (NSGA-II, GDE3, SMPSO and MOEA/D) to minimize two objectives: the ligand-receptor intermolecular energy and the RMSD score. We have prepared a set of instances that includes the wild-type EGFR kinase domain and the same receptor with somatic mutations, and then we assessed the performance of the algorithms by applying a quality indicator to evaluate the convergence and diversity of the reference fronts. The MOEA/D algorithm yields the best solutions to these docking problems. The obtained solutions were analyzed, showing promising results to predict candidate EGFR inhibitors by using this multi-objective approach.

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A Multi-objective Optimization Energy Approach to Predict the Ligand Conformation in a Docking Process

Cited by 10 publications

References 22 publications

Solving molecular flexible docking problems with metaheuristics: A comparative study

Solving molecular flexible docking problems with metaheuristics: A comparative study

A New Multi-objective Approach for Molecular Docking Based on RMSD and Binding Energy

Molecular Docking Optimization in the Context of Multi-Drug Resistant and Sensitive EGFR Mutants

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