scholarly journals A high-throughput virtual screening in Grid for new tubulin-targeted inhibitors of plant fungal pathogens

2018 ◽  
Vol 22 ◽  
pp. 335-339
Author(s):  
P. A. Karpov ◽  
O. M. Demchuk ◽  
O. V. Rayevsky ◽  
S. P. Ozheredov ◽  
S. I. Spivak ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Binding Site ◽  
High Throughput ◽  
Fungal Pathogens ◽  
Pathogenic Fungi ◽  
Phytopathogenic Fungi ◽  
Dynamics Simulation ◽  
Targeted Inhibitors ◽  
Β Tubulin ◽  
High Throughput Virtual Screening

Aim. To select new tubulin-targeted inhibitors of plant fungal pathogens based on results of high-throughput virtual screening in Grid. Methods. Protein and ligand spatial structure modelling (I-Tasser, Grid), design and virtual screening ligands library (UCSF Dock 6, Grid), molecular docking (CCDC Gold), molecular dynamics simulation (Gromacs, Grid). Results. 240 structural models of tubulin molecules (82 α-, 111 β- and 47 γ-tubulin) from 62 species of phytopathogenic fungi were constructed. It was found that imidazole ligands, demonstrate strongest affinity to α- and β-tubulin. It was found that among α-, β- and γ-tubulin, taxol binding site of β-tubulin possess the strongest potential as the fungicidal drugs target. It was selected 50 leader compounds: 23 with affinity for GTP/GDF-exchange site and 27 with affinity for taxol-binding site. Conclusions. It was found, that in phytopathogenic fungi, taxol binding site of β-tubulin are the main fungicid drug target (in compare to other tubulin site or isotype). The highest affinity was predicted for the compounds F0478-0219, F0478-0166 and β-tubulin from Puccinia graminis f. sp. Tritici, as well as for the compound F0478-0385 and β-tubulin from Magnaporthe oryzae. Keywords: pathogenic fungi, fungicides, tubulin, virtual screening, Grid.

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 High throughput screening of tubulin inhibitors from parasitic fungi

1970 ◽  
pp. 319-322
Author(s):  
P. A. Karpov ◽  
O. M. Demchuk ◽  
S. P. Ozheriedov ◽  
S. I. Spivak ◽  
O. V. Raievskyi ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
High Throughput ◽  
High Throughput Screening ◽  
Protein Structures ◽  
Pathogenic Fungi ◽  
Tubulin Inhibitors ◽  
Parasitic Fungi ◽  
High Throughput Virtual Screening

Aim. Implementation of 3D-modeling, molecular dynamics, high-throughput screening and molecular docking for search of new inhibitors of parasitic fungi tubulin. Methods. Protein structures were constructed using I-TASSER server and optimized by Gromacs. Ligands library was prepared in Mopac7 program and screened using UCSF Dock 6. Best ligands were docked in CCDC Gold. Results. It was reconstructed spatial molecular structure for 93 α-, 95 β- and 78 γ-tubulins from 76 species of pathogenic fungi genus: Microsporum, Arthroderma, Histoplasma, Blastomyces, Emmonsia, Uncinocarpus, Coccidioides, Paracoccidioides, Aspergillus, Botrytis cinerea, Sclerotinia, Rhynchosporium, Marssonina, Scedosporium, Fusarium, Gibberella, Candida, Ceraceosorus, Malassezia, Anthracocystis, Melanopsichium, Sporisorium, Ustilago, Cryptococcus, Trichosporon, Mucor, Rhizopus and Lichtheimia. Libraries of 3D-models of parasitic fungi tubulins and perspective ligands were created. Based on results of high-throughput virtual screening, 200 perspective agents were selected from more than 7 million compounds. After resulting molecular docking in CCDC GOLD, we specify 19 leading compounds. We propose these compounds as potent tubulin inhibitors and recommend them for in vitro testing as new fungicides. Conclusions. Based on results of high-throughput virtual screening in Grid, 19 new imidazole inhibitors of parasitic fungi tubulin were selected.Keywords: microtubule, tubulins, fungicides, imidazole derivatives, virtual screening, molecular docking.

scholarly journals High Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Non-Covalent Inhibitor

2021 ◽  
Author(s):  
Austin Clyde ◽  
Stephanie Galanie ◽  
Daniel W. Kneller ◽  
Heng Ma ◽  
Yadu Babuji ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Binding Site ◽  
High Throughput ◽  
Hydrophobic Interactions ◽  
Md Simulations ◽  
X Ray ◽  
X Ray Crystallography ◽  
Biochemical Assays ◽  
Main Protease ◽  
High Throughput Virtual Screening

Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel non-covalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) by employing a scalable high throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this Mpro inhibitor with an inhibition constant (Ki) of 2.9 µM [95% CI 2.2, 4.0]. Further, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple µs-timescale molecular dynamics (MD) simulations, and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design. Significance Statement The ongoing novel coronavirus pandemic (COVID-19) has prompted a global race towards finding effective therapeutics that can target the various viral proteins. Despite many virtual screening campaigns in development, the discovery of validated inhibitors for SARS-CoV-2 protein targets has been limited. We discover a novel inhibitor against the SARS-CoV-2 main protease. Our integrated platform applies downstream biochemical assays, X-ray crystallography, and atomistic simulations to obtain a comprehensive characterization of its inhibitory mechanism. Inhibiting Mpro can lead to significant biomedical advances in targeting SARS-CoV-2 treatment, as it plays a crucial role in viral replication.

Novel thermally activated delayed fluorescence materials by high-throughput virtual screening: going beyond donor–acceptor design

2021 ◽  
Vol 9 (9) ◽  
pp. 3324-3333 ◽  
Author(s):  
Ke Zhao ◽  
Ömer H. Omar ◽  
Tahereh Nematiaram ◽  
Daniele Padula ◽  
Alessandro Troisi
Keyword(s):  
Virtual Screening ◽  
Quantum Chemistry ◽  
High Throughput ◽  
Delayed Fluorescence ◽  
Thermally Activated Delayed Fluorescence ◽  
Thermally Activated ◽  
Quantum Chemistry Calculations ◽  
Donor Acceptor ◽  
Molecular Semiconductors ◽  
High Throughput Virtual Screening

125 potential TADF candidates are identified through quantum chemistry calculations of 700 molecules derived from a database of 40 000 molecular semiconductors. Most of them are new and some do not belong to the class of donor–acceptor molecules.

A novel compound active against SARS-CoV-2 targeting Uridylate-specific endoribonuclease (NendoU/NSP15): In silico and in vitro investigations

2021 ◽  
Author(s):  
Sumit Kumar ◽  
Yash Gupta ◽  
Samantha Zak ◽  
Charu Upadhyay ◽  
Neha Sharma ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
In Silico ◽  
Specific Enzyme ◽  
Cyclic Phosphates ◽  
High Throughput Virtual Screening

NendoU (NSP15) is an Mn(2+)-dependent, uridylate-specific enzyme, which leaves 2'-3'-cyclic phosphates 5' to the cleaved bond. Our in-house library was subjected to high throughput virtual screening (HTVS) to identify compounds...

Accelerating the discovery of energetic melt-castable materials by a high-throughput virtual screening and experimental approach

2021 ◽  
Author(s):  
Siwei Song ◽  
Fang Chen ◽  
Yi Wang ◽  
Kangcai Wang ◽  
Mi Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
High Throughput ◽  
Experimental Approach ◽  
Chemical Data ◽  
Research Paradigm ◽  
New Materials ◽  
High Throughput Virtual Screening

With the growth of chemical data, computation power and algorithms, machine learning-assisted high-throughput virtual screening (ML-assisted HTVS) is revolutionizing the research paradigm of new materials. Herein, a combined ML-assisted HTVS...

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