EDGA: A Population Evolution Direction-Guided Genetic Algorithm for Protein–Ligand Docking

Guan, Boxin; Ning, Jiaxu

doi:10.1089/cmb.2015.0190

Cited by 14 publications

(11 citation statements)

References 23 publications

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“…Molecular docking of thirteen proposed compounds ( Fig. 1 ) with Eg5 binding pocket was analyzed using protein-ligand docking ( 11 ). For this purpose, the crystal structure of the kinesin Eg5 in complex with monastrol (protein data bank (PDB) ID: 1Q0B) was retrieved from the PDB.…”

Section: Methodsmentioning

confidence: 99%

“…Since the position of the co-crystallized monastrol within the binding site of Eg5 was known, we centered the grid box at the centroid of bounded monastrol in the active site (grid box center coordinate: X: 41.501; Y: 15.727; Z: 48.857), so that roughly encompasses the center of the Eg5 binding pocket. For each ligand, 100 independent docking runs were carried out employing the Lamarckian genetic algorithm (LGA) ( 11 ). The factors for LGA were defined as follows: a maximum number of 2.5 × 106 energy evaluations; a maximum number of generations of 27000; mutation and crossover rates of 0.02 and 0.8, respectively.…”

Section: Methodsmentioning

confidence: 99%

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Monastrol derivatives: in silico and in vitro cytotoxicity assessments

et al. 2020

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Background and purpose: Cancer is the leading cause of death in today’s world, therefore the efforts to achieve anticancer drugs with higher potency and fewer side effects have always been conducted by researchers in the field of pharmaceutical chemistry. Monastrol, a cytotoxic small molecule, from dihydropyrimidinone scaffold, is an inhibitor of the kinesin-5 protein. So, efforts to identify more derivatives of this molecule have been of interest. Experimental approach: Some of monastrol’s analogs as Eg5 inhibitors with different substitution patterns were analyzed, synthesized, and their cytotoxic effects were evaluated on MCF-7 and HeLa cancerous cells in vitro using the MTT assay. The structure-activity relationship (SAR) was studied in silico by molecular docking. Findings / Results: Among all proposed structures, in ducking study, those with hydrophobic moieties on the C2-N3 region, those with a hydroxyl group on the phenyl on C4 position, and those with a carboxylic group on C5 were the best candidates. In vitro studies, on the other side, emphasized that monastrol still was the most potent derivative. Another finding was the more moderate activity of synthesized compounds on the HeLa cell compared to the MCF-7 cell line. During different challenges for substitution at 5-position, some earlier reports around the dihydropyrimidinone reactions were questioned. It seems that the change at the position 5 is not merely accessible, as earlier reports claimed. Also, we could not achieve any better cell cytotoxicity by the larger group in the thiourea region or position 5; nonetheless, it seems that the introduction of a methylene group at this position could be beneficial. Conclusion and implications: The initial results of this study were valuable in terms of design and synthesis and will be useful for future investigations.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Monastrol derivatives: in silico and in vitro cytotoxicity assessments

et al. 2020

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“…Li et al 18 used an entropybased GA with multi-population evolution to solve the docking problem and created a coefficient adaptive scoring function that greatly improves the docking accuracy. Guan et al 19 presented a novel and robust optimization algorithm based on the LGA. This algorithm applied a population evolution direction-guided model of genetics and could find solutions with lower energy for flexible protein-ligand docking problems.…”

Section: Related State-of-the-art Stochastic Optimization Methodsmentioning

confidence: 99%

“…Guan et al. 19 presented a novel and robust optimization algorithm based on the LGA. This algorithm applied a population evolution direction-guided model of genetics and could find solutions with lower energy for flexible protein–ligand docking problems.…”

Section: Related State-of-the-art Stochastic Optimization Methodsmentioning

confidence: 99%

Probing molecular docking problem by an improved quantum-behaved particle swarm optimization algorithm

Fu¹,

Martino²,

Zhao³

2019

Journal of Algorithms & Computational Technology

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The main objective of molecular docking is to find a model of interaction between a protein and ligand with a minimum binding energy. This process is driven by intricate algorithms and scoring functions. This paper mainly concentrates on the search algorithm used for solving the docking problem. Here, a new approach is proposed for the molecular docking problem that utilizes a hybrid algorithm that combines an improved quantum-behaved particle swarm optimization algorithm (QPSO) and the Solis and Wets algorithm. The improved QPSO algorithm that is based on individual particle evolutionary processes is known as individual particle evolutionary particle swarm optimization (IEQPSO). The IEQPSO algorithm was tested and compared with particle swarm optimization, QPSO, and its variants with a suite of benchmark functions. The results indicated the superiority of the proposed approach according to benchmark test functions. Then, the hybrid algorithm based on the IEQPSO algorithm was used for optimizing the energy function of the molecular docking problem and was compared with the classical Lamarckian genetic algorithm used by molecular docking software. Molecular docking and molecular dynamics simulation experiments revealed the effectiveness and feasibility of the proposed algorithm in solving the molecular docking problem.

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“…(1) CE crossover is proposed to optimize crossover operation of the algorithm [ 34 ]. CE crossover uses history information to retain individuals with good genes, and these individuals make the subsequent population better; (2) ED muation is proposed to guide the evolutionary direction according to running historical information [ 35 ]; (3) Binary space partitioning (BSP) tree is employed to maintain the diversity of the individuals in a population [ 36 ]. The BSP tree can memorize all of the evaluated solutions so as to avoid solution re-evaluation.…”

Section: Introductionmentioning

confidence: 99%

HIGA: A Running History Information Guided Genetic Algorithm for Protein–Ligand Docking

Guan

Zhao

2017

Molecules

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Protein-ligand docking is an essential part of computer-aided drug design, and it identifies the binding patterns of proteins and ligands by computer simulation. Though Lamarckian genetic algorithm (LGA) has demonstrated excellent performance in terms of protein-ligand docking problems, it can not memorize the history information that it has accessed, rendering it effort-consuming to discover some promising solutions. This article illustrates a novel optimization algorithm (HIGA), which is based on LGA for solving the protein-ligand docking problems with an aim to overcome the drawback mentioned above. A running history information guided model, which includes CE crossover, ED mutation, and BSP tree, is applied in the method. The novel algorithm is more efficient to find the lowest energy of protein-ligand docking. We evaluate the performance of HIGA in comparison with GA, LGA, EDGA, CEPGA, SODOCK, and ABC, the results of which indicate that HIGA outperforms other search algorithms.

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EDGA: A Population Evolution Direction-Guided Genetic Algorithm for Protein–Ligand Docking

Cited by 14 publications

References 23 publications

Monastrol derivatives: in silico and in vitro cytotoxicity assessments

Monastrol derivatives: in silico and in vitro cytotoxicity assessments

Probing molecular docking problem by an improved quantum-behaved particle swarm optimization algorithm

HIGA: A Running History Information Guided Genetic Algorithm for Protein–Ligand Docking

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