Supercomputer Docking

doi:10.14529/jsfi190302

Cited by 5 publications

(8 citation statements)

References 102 publications

(152 reference statements)

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“…Moreover, for flexible ligands with many torsion degrees of freedom (like, for instance for the compound shown in Fig. 1), the multi-processor performance of the docking program at several dozen or hundred computing cores of a supercomputer is important [8,12]. Therefore, molecular docking of the selected compound to the enzyme surface is a major step in characterization of noncovalent inhibitors and it constitutes a preliminary step in the design of covalent inhibitors.…”

Section: Methodsmentioning

confidence: 99%

“…This approach differs from a common strategy to propose inhibiting molecules, which form non-covalently bound complexes with an enzyme, thus blocking the entrance to the active site for natural substrates. From the computational side, the latter strategy requires an application of algorithms of molecular docking and classical molecular dynamics, which are successfully used in high performance computer simulations [12]. If our aim is to predict a covalent inhibitor, a broader range of computational tools is necessary, including quantum chemistry based methods [1], which require supercomputer resources.…”

Section: Introductionmentioning

confidence: 99%

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Computational Modeling of the SARS-CoV-2 Main Protease Inhibition by the Covalent Binding of Prospective Drug Molecules

2020

JSFI

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We illustrate modern modeling tools applied in the computational design of drugs acting as covalent inhibitors of enzymes. We take the Main protease (M pro) from the SARS-CoV-2 virus as an important present-day representative. In this work, we construct a compound capable to block M pro , which is composed of fragments of antimalarial drugs and covalent inhibitors of cysteine proteases. To characterize the mechanism of its interaction with the enzyme, the algorithms based on force fields, including molecular mechanics (MM), molecular dynamics (MD) and molecular docking, as well as quantum-based approaches, including quantum chemistry and quantum mechanics/molecular mechanics (QM/MM) methods, should be applied. The use of supercomputers is indispensably important at least in the latter approach. Its application to enzymes assumes that energies and forces in the active sites are computed using methods of quantum chemistry, whereas the rest of protein matrix is described using conventional force fields. For the proposed compound, containing the benzoisothiazolone fragment and the substitute at the uracil ring, we show that it can form a stable covalently bound adduct with the target enzyme, and thus can be recommended for experimental trials.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational Modeling of the SARS-CoV-2 Main Protease Inhibition by the Covalent Binding of Prospective Drug Molecules

2020

JSFI

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“…Molecular docking is widely used in today's virtual screening of new pharmaceutical agents [39,42,67]. In contrast to similarity assessment and machine learning, docking requires significantly more computational resources.…”

Section: Molecular Modelingmentioning

confidence: 99%

Computational Approaches to Identify a Hidden Pharmacological Potential in Large Chemical Libraries

Druzhilovskiy

Stolbov

Savosina

et al. 2020

JSFI

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To improve the discovery of more effective and less toxic pharmaceutical agents, large virtual repositories of synthesizable molecules have been generated to increase the explored chemicalpharmacological space diversity. Such libraries include billions of structural formulae of druglike molecules associated with data on synthetic schemes, required building blocks, estimated physical-chemical parameters, etc. Clearly, such repositories are "Big Data". Thus, to identify the most promising compounds with the required pharmacological properties (hits) among billions of available opportunities, special computational methods are necessary. We have proposed using a combined computational approach, which combines structural similarity assessment, machine learning, and molecular modeling. Our approach has been validated in a project aimed at finding new pharmaceutical agents against HIV/AIDS and associated comorbidities from the Synthetically Accessible Virtual Inventory (SAVI), a 1.75 billion compound database. Potential inhibitors of HIV-1 protease and reverse transcriptase and agonists of toll-like receptors and STING, affecting innate immunity, were computationally identified. The activity of the three synthesized compounds has been confirmed in a cell-based assay. These compounds belong to the chemical classes, in which the agonistic effect on TLR 7/8 had not been previously shown. Synthesis and biological testing of several dozens of compounds with predicted antiretroviral activity are currently taking place at the NCI/NIH. We also carried out virtual screening among one billion substances to find compounds potentially possessing anti-SARS-CoV-2 activity. The selected hits' information has been accepted by the European Initiative "JEDI Grand Challenge against COVID-19" for synthesis and further biological evaluation. The possibilities and limitations of the approach are discussed.

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“…At present, there are several tens of docking programs. Comprehensive reviews on docking can be found in recent publications [7][8][9] and references therein. The performance of most of docking programs is based on the docking paradigm that assumes that the position of the bound ligand in the active site of the target protein is near the global minimum of the energy of the protein-ligand complex.…”

Section: Introductionmentioning

confidence: 99%

Quantum-Chemical Quasi-Docking for Molecular Dynamics Calculations

et al. 2022

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The quantum quasi-docking procedure is used to compare the docking accuracies of two quantum-chemical semiempirical methods, namely, PM6-D3H4X and PM7. Quantum quasi-docking is an approximation to quantum docking. In quantum docking, it is necessary to search directly for the global minimum of the energy of the protein-ligand complex calculated by the quantum-chemical method. In quantum quasi-docking, firstly, we look for a wide spectrum of low-energy minima, calculated using the MMFF94 force field, and secondly, we recalculate the energies of all these minima using the quantum-chemical method, and among these recalculated energies we determine the lowest energy and the corresponding ligand position. Both PM6-D3H4X and PM7 are novel methods that describe well-dispersion interactions, hydrogen and halogen bonds. The PM6-D3H4X and PM7 methods are used with the COSMO implicit solvent model as it is implemented in the MOPAC program. The comparison is made for 25 high quality protein-ligand complexes. Firstly, the docking positioning accuracies have been compared, and we demonstrated that PM7+COSMO provides better positioning accuracy than PM6-D3H4X. Secondly, we found that PM7+COSMO demonstrates a much higher correlation between the calculated and measured protein–ligand binding enthalpies than PM6-D3H4X. For future quantum docking PM7+COSMO is preferable, but the COSMO model must be improved.

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Supercomputer Docking

Cited by 5 publications

References 102 publications

Computational Modeling of the SARS-CoV-2 Main Protease Inhibition by the Covalent Binding of Prospective Drug Molecules

Computational Modeling of the SARS-CoV-2 Main Protease Inhibition by the Covalent Binding of Prospective Drug Molecules

Computational Approaches to Identify a Hidden Pharmacological Potential in Large Chemical Libraries

Quantum-Chemical Quasi-Docking for Molecular Dynamics Calculations

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