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

Guan, Boxin; Zhao, Yuhai

doi:10.3390/molecules22122233

Cited by 6 publications

(5 citation statements)

References 41 publications

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“…For each tested algorithm, the number of iterations was 27,000 and the number of individuals was 100. The other parameters were set in accordance with previous studies [31,32,33,34,35], and the details are shown in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…The ideal search algorithm should be able to enumerate all possible binding poses between ligands and receptors, but this is difficult to achieve because the search space involved in molecular docking is huge. Many evolutionary computation methods have been presented for solving protein–ligand docking problems [22,23,24,25,26,27,28]: for example, simulated annealing (SA) [29], genetic algorithm (GA) [30], Lamarckian genetic algorithm (LGA) [31], running history information guided genetic algorithm (HIGA) [32], and swarm optimization for highly flexible protein–ligand docking (SODOCK) [33]. These methods have been applied to solve docking problems, but they have some drawbacks.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient ABC_DE_Based Hybrid Algorithm for Protein–Ligand Docking

Guan

Zhao

2018

IJMS

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Protein–ligand docking is a process of searching for the optimal binding conformation between the receptor and the ligand. Automated docking plays an important role in drug design, and an efficient search algorithm is needed to tackle the docking problem. To tackle the protein–ligand docking problem more efficiently, An ABC_DE_based hybrid algorithm (ADHDOCK), integrating artificial bee colony (ABC) algorithm and differential evolution (DE) algorithm, is proposed in the article. ADHDOCK applies an adaptive population partition (APP) mechanism to reasonably allocate the computational resources of the population in each iteration process, which helps the novel method make better use of the advantages of ABC and DE. The experiment tested fifty protein–ligand docking problems to compare the performance of ADHDOCK, ABC, DE, Lamarckian genetic algorithm (LGA), running history information guided genetic algorithm (HIGA), and swarm optimization for highly flexible protein–ligand docking (SODOCK). The results clearly exhibit the capability of ADHDOCK toward finding the lowest energy and the smallest root-mean-square deviation (RMSD) on most of the protein–ligand docking problems with respect to the other five algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient ABC_DE_Based Hybrid Algorithm for Protein–Ligand Docking

Guan

Zhao

2018

IJMS

Self Cite

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“…24 Prochek Ramachandran plot was used for the validation of target protein (2i54) defined by the phi (j) and psi (y) angles, the number of amino acid residues shown in the most favorable region is 90.8%, the additional allowed region is 8.9%, generously allowed regions are 0.3% and the Lamarckian Genetic Algorithm. 30 After each protein-ligand complex interactions, among the 9 poses, the best pose based on its conformation and binding affinity was selected, and obtained RMSD (Root Mean Square Deviation) values. 31 The RMSD values (UB/LB) zero refers to good interaction between protein and ligand.…”

Section: Target Preparation and Validationmentioning

confidence: 99%

Drug Repurposing against Phosphomannomutase for the Treatment of Cutaneous Leishmaniasis

Sheikh

Ansari²,

Hassan

et al. 2023

Orient. J. Chem

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Due to the lack of approved vaccines against Cutaneous leishmaniasis (CL), chemotherapy is the only treatment option. Presently, none of the current CL drugs have high levels of efficacy and safety profiles. Thus, the development of new and safer drugs is urgently needed. Drug repurposing can be used for the development of new therapeutic activities. Phosphomannomutase (PMM) has become highlighted as a potential drug target due to its important role in the biosynthesis of glycoconjugates which is essential for parasite virulence. To identify new promising lead molecules, we have performed virtual screening of 8,500 drugs and selected 46 drugs for docking simulation through the Glide module of Schrodinger software. The saquinavir and grazoprevir showed the highest binding affinity (-10.144 and -10.131 kcal/mole). To find the stability of both complexes, molecular dynamics (MD) simulations were performed at 100ns. The grazoprevir-2i54 and saquinavir-2i54 complexes showed good stability in the active site of the receptor. It could be an alternative drug for the treatment of CL.

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“…PyRx 0.8, along with AutoDock Vina v.1.1 28 , was used for structure-based docking studies of Mpro with FDA approved ligand molecules. The software was selected owing to its rapid yet efficient search capability to find accurate and active conformations 29 . While the SARS-CoV2 Mpro protein molecule was considered as a rigid structure, ligand drug molecules were considered flexible during the docking process.…”

Section: -Structure-based Virtual Screeningmentioning

confidence: 99%

Old Arsenal to Combat New Enemy: Repurposing of Commercially Available FDA Approved Drugs Against Main Protease of SARS-CoV2.

Singh

Srivastava²,

Mishra³

et al. 2020

Preprint

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In lack of vaccination and therapeutic drugs, the ongoing COVID-19 pandemic affected millions of people, causing 1,018,957 deaths worldwide (World health organization; 1st October 2020). The conventional drug design pipeline for effective and safer drug development is a costly and time-intensive affair. It takes around ten years in general from identifying a clinical candidate to get the approvals for actual applications. An effective way to cut short drug design pipeline in such emergency cases could be the repurposing of already approved drugs against novel targets. Here in this work, we explored the structure-based drug screening approach to find potential inhibitors of SARS-CoV2 main protease (Mpro) from the library of already FDA approved commercially available drugs. The site-specific and blind docking studies, in combination, suggest three potential inhibitors of Mpro, Ergotamine (ZINC000052955754), Nilotinib (ZINC000006716957) and Naldemedine (ZINC000100378061). Molecular dynamics (MD) simulations and binding free energy calculations using the MMPBSA method further reinforced the efficiency of the screened Mpro inhibitor candidates. The work yields enough evidence to conduct rigorous experimental validation of these drugs before utilizing them for the therapeutic management of SARS-CoV2 infection.

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

Cited by 6 publications

References 41 publications

An Efficient ABC_DE_Based Hybrid Algorithm for Protein–Ligand Docking

An Efficient ABC_DE_Based Hybrid Algorithm for Protein–Ligand Docking

Drug Repurposing against Phosphomannomutase for the Treatment of Cutaneous Leishmaniasis

Old Arsenal to Combat New Enemy: Repurposing of Commercially Available FDA Approved Drugs Against Main Protease of SARS-CoV2.

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