Simulation with quantum mechanics/molecular mechanics for drug discovery

Barbault, Florent; Maurel, François

doi:10.1517/17460441.2015.1076389

Cited by 26 publications

(14 citation statements)

References 72 publications

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“…Zhang et al first studied the initial step of the acylation reaction by combined ab initio QM/MM. QM/MM is a hybrid computational method that aims to combine the strengths of the QM (accuracy) and MM (speed) approaches, through exerting a QM calculation on the ligand binding interactions, the core region, and a MM calculation on the rest of the system [ 92 , 93 ]. Given that the QM/MM approach is able to explore chemical bond formation, it can be utilized to study the catalytic mechanism of an enzyme and provide useful information for the rational design of mechanism-based inhibitors.…”

Section: Resultsmentioning

confidence: 99%

Computational Studies on Acetylcholinesterases

Cheng

Sussman

et al. 2017

Molecules

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Functions of biomolecules, in particular enzymes, are usually modulated by structural fluctuations. This is especially the case in a gated diffusion-controlled reaction catalyzed by an enzyme such as acetylcholinesterase. The catalytic triad of acetylcholinesterase is located at the bottom of a long and narrow gorge, but it catalyzes the extremely rapid hydrolysis of the neurotransmitter, acetylcholine, with a reaction rate close to the diffusion-controlled limit. Computational modeling and simulation have produced considerable advances in exploring the dynamical and conformational properties of biomolecules, not only aiding in interpreting the experimental data, but also providing insights into the internal motions of the biomolecule at the atomic level. Given the remarkably high catalytic efficiency and the importance of acetylcholinesterase in drug development, great efforts have been made to understand the dynamics associated with its functions by use of various computational methods. Here, we present a comprehensive overview of recent computational studies on acetylcholinesterase, expanding our views of the enzyme from a microstate of a single structure to conformational ensembles, strengthening our understanding of the integration of structure, dynamics and function associated with the enzyme, and promoting the structure-based and/or mechanism-based design of new inhibitors for it.

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Section: Resultsmentioning

confidence: 99%

Computational Studies on Acetylcholinesterases

Cheng

Sussman

et al. 2017

Molecules

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“…In this In-Silico effort we prepared mol2 les of the essential dataset of the chemical structures from the two supplementary e-Drug3D collections which were carefully constructed and have been shown similar inhibition in infectious models and resulting from (i) the generation of multiple conformers of high-quality and curated structures of FDA-approved drugs with molecular weight less than 2000 for the: {[5-(1lambda4,3lambda4,7lambda4-purin-6-ylsulfanyl)-1-methyl-4H-1lambda5-imidazol-4ylidene](oxo)imino}-lambda1-oxidanylidene-heptahydrogen ring systems and (ii) the calculation of the most probable ionic and tautomeric states at pH 7.4. Smaller drugs (backbone with less than 20 heavy atoms) were also prepared in the .pdb, .pdbqt and .mol2 format les (26,(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(46)(47)(48) present in the e-Drug3D dataset. (28,(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48) The ensemble of 3D molecule conformations (31,(32)…”

Section: Preparation Of the Datasets With Known E-drug3d Drugsmentioning

confidence: 99%

“…For this purpose, (1,2,4-6,23) we initially selected the three-dimensional structures of the non-structural proteins of the Nsp3, Nsp5 (PLpro domain), (1-5,6,7-48) Nsp12 (RdRp), the structural Spike proteins, the Nsp15 (endoribonuclease), and the nucleocapsid protein (N protein) between the SARS-CoV-2 Mpro structure and the SARS-CoV Mpro, PDB entry 6LU7 as the best-characterized drug targets among coronaviruses. (2,5,6,7,(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27) For the N protein, we clustered 31 conformations with (11,13,(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)32) Glu174 present in an opened residue conformation out of a total of 40 ionization states (16,19,47,48) present in the globular clu...…”

Section: Preparation Of the Protein Structuresmentioning

confidence: 99%

“…(27,28) Compound name, gene expression data, metabolic associations, evolutionary analysis, commercial name or generic name of each drug and protein-drug datasets were included and (29,30) (46,48) connecting 362 drugs were retained. We also compiled clinically reported adverse (30,32,(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)48) drug-drug interactions (DDIs) data from the (38,39,40,(42)(43)(44)(45)(46)(47)(48) the Therapeutic Target Database (TTD, v4.3.02)41, the PharmGKB database (December 30, 2015) and the FDA approved DrugBank databases using reported binding a nity data such as the dissociation constant (Kd), inhibition constant/potency (Ki), median inhibitory concentration (IC50) ≤ 10 µM or median effective concentration (EC50). Drug-Protein target interactions, (42)(43)(44)(45)(46)(47)(48) Chemical similarity analysis and (32,35,(36)(37)(38)(39)(40)…”

Section: Collecting Gold-standard Pairwise Drug Combinationsmentioning

confidence: 99%

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Quantum Deep Learning Functional Similarities on Remdesivir, Drug Synergies to Treat COVID-19 in Practice.

Grigoriadis

2020

Preprint

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Νovel SARS coronavirus 2 (SARS-CoV-2) of the family Coronaviridae starting in China and spreading around the world is an enveloped, positive-sense, single-stranded RNA of the genus betacoronavirus encoding the SARS-COV-2 (2019-NCOV, Coronavirus Disease 2019. Remdesivir drug, or GS-5734 lead compound, first described in 2016 as a potential anti-viral agent for Ebola diseade and has also being researched as a potential therapeutic agent against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus that causes coronavirus disease 2019 (COVID-19). Computer-aided drug design (CADD), Structure and Ligand based Drug Repositioning strategies based on parallel docking methodologies have been widely used for both modern drug development and drug repurposing to find effective treatments against this disease. Quantum mechanics, molecular mechanics, molecular dynamics (MD), and combinations have shown superior performance to other drug design approaches providing an unprecedented opportunity in the rational drug development fields and for the developing of innovative drug repositioning methods. We tested 18 phytochemical small molecule libraries and predicted their synergies in COVID-19 (2019- NCOV), to devise therapeutic strategies, repurpose existing ones in order to counteract highly pathogenic SARS-CoV-2 infection and the BRD4- conserved residue associated COVID-19 pathology. We anticipate that our quantum deep learing similarity approaches can be used for the development of anticoronaviral drug combinations in large scale HTS screenings, and to maximize the safety and efficacy of the Remdesivir, Colchicine and Ursolic acid drugs already known to induce synergy with potential therapeutic value or drug repositioning to COVID-19 patients.

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“…The hybrid QM/MM computation is a compromise between the speed and accuracy, which allows for detailed analysis of ligand association for understanding or predicting their interactions. The QM/MM methods have been widely employed during the last decades to study chemical processes such as enzyme-inhibitor interactions [192].…”

Section: Qm/mm Studymentioning

confidence: 99%