scholarly journals COVID-19 Repurposed Therapeutics Targeting the Viral Protease and Spike-protein:ACE2 Interface using MD-based Pharmacophore and Consensus Virtual Screening

2020 ◽  
Author(s):  
brady garabato ◽  
Federico Falchi ◽  
Andrea Cavalli
Keyword(s):  
Molecular Dynamics ◽  
Virtual Screening ◽  
Crystal Structures ◽  
High Throughput ◽  
Binding Energies ◽  
Enhanced Sampling ◽  
Viral Protease ◽  
High Affinity ◽  
Receptor Interface

<p>Molecular dynamics (MD) and enhanced sampling MD was performed for 100 ns on the biological assembly of the COVID-19 protease (<a href="https://www.rcsb.org/structure/6lu7">6LU7</a>), and a template of the COVID-19 S-protein:ACE2 receptor interface (99.88% coverage of 6M0J; model03, <a href="https://swissmodel.expasy.org/interactive/HLkhkP/models/">swissmodel</a>). Apo-site pharmacophores of the resulting structural clusters were used to mine the FDA database (8700 compounds), and a multi-target library was developed from MD-based hits in high affinity sites across 100 ns. Consensus hits from high throughput docking in crystal structures 5R82, 6LU7 and 6Y2F (protease), and 6VW1 (S-protein:ACE2) were also added, and the resulting libraries were re-docked into MD sites to collect potential COVID-19 re-purposed therapeutics by estimated binding energies. </p>

scholarly journals COVID-19 Repurposed Therapeutics Targeting the Viral Protease and Spike-protein:ACE2 Interface using MD-based Pharmacophore and Consensus Virtual Screening

2020 ◽  
Author(s):  
brady garabato ◽  
Federico Falchi ◽  
Andrea Cavalli
Keyword(s):  
Molecular Dynamics ◽  
Virtual Screening ◽  
Crystal Structures ◽  
High Throughput ◽  
Binding Energies ◽  
Enhanced Sampling ◽  
Viral Protease ◽  
High Affinity ◽  
Receptor Interface

<p>Molecular dynamics (MD) and enhanced sampling MD was performed for 100 ns on the biological assembly of the COVID-19 protease (<a href="https://www.rcsb.org/structure/6lu7">6LU7</a>), and a template of the COVID-19 S-protein:ACE2 receptor interface (99.88% coverage of 6M0J; model03, <a href="https://swissmodel.expasy.org/interactive/HLkhkP/models/">swissmodel</a>). Apo-site pharmacophores of the resulting structural clusters were used to mine the FDA database (8700 compounds), and a multi-target library was developed from MD-based hits in high affinity sites across 100 ns. Consensus hits from high throughput docking in crystal structures 5R82, 6LU7 and 6Y2F (protease), and 6VW1 (S-protein:ACE2) were also added, and the resulting libraries were re-docked into MD sites to collect potential COVID-19 re-purposed therapeutics by estimated binding energies. </p>

scholarly journals High-Throughput Virtual Screening, Molecular Dynamics Simulation, and Enzyme Kinetics Identified ZINC84525623 as a Potential Inhibitor of NDM-1

2019 ◽  
Vol 20 (4) ◽  
pp. 819 ◽  
Author(s):  
Md Rehman ◽  
Mohamed AlAjmi ◽  
Afzal Hussain ◽  
Gulam Rather ◽  
Meraj Khan
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Virtual Screening ◽  
Enzyme Kinetics ◽  
High Throughput ◽  
Md Simulation ◽  
Dynamics Simulation ◽  
Multi Drug Resistance ◽  
Stable Complex ◽  
High Throughput Virtual Screening

The bacteria expressing New Delhi Metallo-β-lactamase-1 (NDM-1) can hydrolyze all β-lactam antibiotics including carbapenems, causing multi-drug resistance. The worldwide emergence and dissemination of gene blaNDM-1 (produces NDM-1) in hospital and community settings, rising problems for public health. Indeed, there is an urgent need for NDM-1 inhibitors to manage antibiotic resistance. Here, we have identified novel non-β-lactam ring-containing inhibitors of NDM-1 by applying a high-throughput virtual screening of lead-like subset of ZINC database. The screened compounds were followed for the molecular docking, the molecular dynamics simulation, and then enzyme kinetics assessment. The adopted screening procedure funnels out five novel inhibitors of NDM-1 including ZINC10936382, ZINC30479078, ZINC41493045, ZINC7424911, and ZINC84525623. The molecular mechanics-generalized born surface area and molecular dynamics (MD) simulation showed that ZINC84525623 formed the most stable complex with NDM-1. Furthermore, analyses of the binding pose after MD simulation revealed that ZINC84525623 formed two hydrogen bonds (electrostatic and hydrophobic interaction) with key amino acid residues of the NDM-1 active site. The docking binding free energy and docking binding constant for the ZINC84525623 and NDM-1 interaction were estimated to be −11.234 kcal/mol, and 1.74 × 108 M−1 respectively. Steady-state enzyme kinetics in the presence of ZINC84525623 show the decreased catalytic efficiency (i.e., kcat/Km) of NDM-1 on various antibiotics. The findings of this study would be helpful in identifying novel inhibitors against other β-lactamases from a pool of large databases. Furthermore, the identified inhibitor (ZINC84525623) could be developed as efficient drug candidates.

scholarly journals Working Mechanism of Remdesivir and Discovering New Potential Inhibitors to SARS-CoV-2 RNA-Dependent RNA Polymerase (RdRp) using High-Throughput Virtual Screening and Molecular Dynamics Simulations .

2021 ◽  
Author(s):  
Dylan Brunt ◽  
Phillip Lakernick ◽  
CHUN WU
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Virtual Screening ◽  
Rna Polymerase ◽  
High Throughput ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Rna Dependent Rna Polymerase ◽  
Zinc Database ◽  
High Throughput Virtual Screening

Abstract RNA-dependent RNA polymerase (RdRp), is an enzyme essential component in the RNA replication within the life cycle of the severely acute respiratory coronavirus-2 (SARS-CoV-2), causing the deadly respiratory induced sickness COVID-19. Remdesivir is a prodrug that has seen some success in inhibiting this enzyme, however there is still the pressing need for effective alternatives. In this study, we present the discovery of four non-nucleoside small molecules that bind favorably to RdRp over adenosine-triphosphate (ATP) and active-form remdesivir-triphosphate (RTP) using high-throughput virtual screening (HTVS) coupled with extensive (total 4800 ns) molecular dynamics (MD) simulations with using the ZINC compounds database against SARS-CoV-2 RdRp (PDB: 7BV2). We found that the simulations with both ATP and RTP remained stable for the duration of their trajectories, and it was revealed that the phosphate tail of RTP was stabilized by a positive amino acid pocket near the entry channel of RTP and magnesium ions containing residues K551, R553, R555 and K621. It was also found that residues D623, D760, and N691 further stabilized the ribose portion of RTP with U10 on the template RNA strand forming hydrogen pairs with the adenosine motif. Using these models of RdRp, we employed them to screen the ZINC database of ~17 million molecules. Using docking and drug properties scoring, we narrowed down our selection to fourteen candidates. These were subjected to 200 ns simulations each underwent free energy calculations. We identified four hit compounds from the ZINC database that have similar binding poses to RTP while possessing lower overall binding free energies, with ZINC097971592 having a binding free energy two times lower than RTP.

scholarly journals Ligand discrimination during virtual screening of the CB1 cannabinoid receptor crystal structures following cross-docking and microsecond molecular dynamics simulations

RSC Advances ◽  
2019 ◽  
Vol 9 (28) ◽  
pp. 15949-15956 ◽  
Author(s):  
Jason S. E. Loo ◽  
Abigail L. Emtage ◽  
Lahari Murali ◽  
Sze Siew Lee ◽  
Alvina L. W. Kueh ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Virtual Screening ◽  
Molecular Dynamics Simulations ◽  
Crystal Structures ◽  
Cannabinoid Receptor ◽  
Cross Docking ◽  
Cb1 Cannabinoid Receptor ◽  
Screening Performance ◽  
Dynamics Simulations ◽  
Virtual Screening Performance

Ligands of inactive and active-state CB1 receptor crystal structures were swapped and virtual screening performance assessed after molecular dynamics simulations.

scholarly journals Discovery of Novel Tankyrase Inhibitors through Molecular Docking-Based Virtual Screening and Molecular Dynamics Simulation Studies

Molecules ◽  
2020 ◽  
Vol 25 (14) ◽  
pp. 3171 ◽  
Author(s):  
Vladimir P. Berishvili ◽  
Alexander N. Kuimov ◽  
Andrew E. Voronkov ◽  
Eugene V. Radchenko ◽  
Pradeep Kumar ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Molecular Dynamics Simulations ◽  
Binding Energies ◽  
Dynamics Simulation ◽  
Ic50 Values ◽  
Energy Perturbation ◽  
Dynamics Simulations

Tankyrase enzymes (TNKS), a core part of the canonical Wnt pathway, are a promising target in the search for potential anti-cancer agents. Although several hundreds of the TNKS inhibitors are currently known, identification of their novel chemotypes attracts considerable interest. In this study, the molecular docking and machine learning-based virtual screening techniques combined with the physico-chemical and ADMET (absorption, distribution, metabolism, excretion, toxicity) profile prediction and molecular dynamics simulations were applied to a subset of the ZINC database containing about 1.7 M commercially available compounds. Out of seven candidate compounds biologically evaluated in vitro for their inhibition of the TNKS2 enzyme using immunochemical assay, two compounds have shown a decent level of inhibitory activity with the IC50 values of less than 10 nM and 10 μM. Relatively simple scores based on molecular docking or MM-PBSA (molecular mechanics, Poisson-Boltzmann, surface area) methods proved unsuitable for predicting the effect of structural modification or for accurate ranking of the compounds based on their binding energies. On the other hand, the molecular dynamics simulations and Free Energy Perturbation (FEP) calculations allowed us to further decipher the structure-activity relationships and retrospectively analyze the docking-based virtual screening performance. This approach can be applied at the subsequent lead optimization stages.

scholarly journals Virtual screening identifies novel high-affinity σ1 receptor ligands

2019 ◽  
Author(s):  
Daniel A. Greenfield ◽  
Hayden R. Schmidt ◽  
Piotr Sliz ◽  
Andrew C. Kruse
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
Transmembrane Protein ◽  
Cross Reactivity ◽  
Receptor Ligands ◽  
Computational Docking ◽  
High Affinity ◽  
Σ1 Receptor ◽  
Ligand Discovery

AbstractThe σ1 receptor is a transmembrane protein implicated in several pathophysiological conditions, including neurodegenerative disease1, drug addiction2, cancer3, and pain4. However, there are no high-throughput functional assays for σ1 receptor drug discovery. Here, we assessed high-throughput structure-based computational docking for discovery of novel ligands of the σ1 receptor. We screened a library of over 6 million compounds using the Schrödinger Glide package, followed by experimental characterization of top-scoring candidates. 77% of tested candidates bound σ1 with high affinity (10-550 nM). These include compounds with high selectivity for the σ1 receptor compared to the genetically unrelated but pharmacologically similar σ2 receptor, as well as compounds with substantial cross-reactivity between the two receptors. These results establish structure-based virtual screening as a highly effective platform for σ1 receptor ligand discovery.

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