High throughput virtual screening based discovery of dengue protease inhibitor

2017 ◽  
Vol 4 (3) ◽  
pp. 35-40
Author(s):  
Sounak Bagchi ◽  
Uzma Alia ◽  
Faiz Mohammad ◽  
Mohd Usman Mohd Siddique
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Protease Inhibitor ◽  
Protease Inhibitors ◽  
High Throughput ◽  
Active Site ◽  
Binding Mode ◽  
Mode Analysis ◽  
Active Site Residues ◽  
High Throughput Virtual Screening

High throughput virtual screening (HTVS) has been proved a successful tool for getting LEADs in drug design and discovery. In an attempt to design new Dengue protease inhibitors, we performed HTVS using Zinc13 database containing 13,195,609 drug-like molecules. ZINC42678127 was identified as potential HIT against Dengue protease. It’s shape and electrostatic complimentary was found to be 0.608 and 0.078, respectively. Qikprop analysis of the compound complied with the Rule of Five (Ro5) and other drug- likeliness properties. Binding mode analysis of docked conformer of ZINC42678127, displayed favorable interaction with the active site residues of DENV protease. The identified HIT has a potential to become a LEAD against Dengue protease.

scholarly journals Binding site analysis of potential protease inhibitors of COVID-19 using AutoDock

2020 ◽  
Author(s):  
DIPTI MOTHAY ◽  
K.V. RAMESH
Keyword(s):  
Protease Inhibitor ◽  
Protease Inhibitors ◽  
Active Site ◽  
Molecular Interaction ◽  
3D Structure ◽  
Active Site Residues ◽  
Multiple Sequence ◽  
Site Analysis ◽  
Interaction Studies ◽  
Structural Insights

Abstract Recent outbreak of COVID-19 caused by SARS-CoV-2 in December 2019 raised global health concerns. Re-purposing the available protease inhibitor drugs for immediate use in treatment in SARS‐CoV‐2 infections could improve the currently available clinical management. The current study, aims to predict theoretical structure for protease of COVID-19 and to explore further whether this protein can serve as a target for protease inhibitor drugs such as remdesivir, nelfinavir, lopinavir, ritonavir and α –ketoamide. While the 3D structure of protease was predicted using SWISS MODEL server, molecular interaction studies between protein and ligands were performed using AutoDock software. The predicted protease model was reasonably good based on reports generated by different validation servers. The study further revealed that all the protease inhibitor drugs got docked with negative dock energy onto the target protein. Molecular interaction studies showed that protease structure had multiple active site residues for remdesivir, while for remaining ligands the structure had only one active site residue each. From the output of multiple sequence alignment, it is evident that ligand binding sites were conserved. The current in-silico study thus, provides structural insights about the protease of COVID-19 and also its molecular interactions with some of the known protease inhibitors.

scholarly journals Virtual Screening of Heterocyclic Molecules to Identify Potential SARS-COV2 virus Mpro Protease Inhibitors for Further Medicinal Chemistry design

2020 ◽  
Author(s):  
Mansour Sobeh ◽  
Reda Ben Mrid ◽  
Abdelaziz Yasri
Keyword(s):  
Virtual Screening ◽  
Protease Inhibitors ◽  
Medicinal Chemistry ◽  
Binding Mode ◽  
Binding Energies ◽  
Mode Analysis ◽  
Receptor Interaction ◽  
Heterocyclic Molecules ◽  
Pubchem Database ◽  
Docking Approach

Abstract In this work, we aimed to identify potential SARS-COV2 virus Mpro protease inhibitors using molecular docking. As a validation step of our docking approach, we used 7 known Mpro inhibitors and predicted their binding energies. We found a very good correlation (R = -0.86) between the binding energies and the pIC50 (-LogIC50) values. We then undertook virtual screening of 50753 heterocyclic molecules from the PubChem Database. The screened molecules were first filtered out using Lipinski, Veber and Ghose rules resulting in 21 142 which was submitted to the docking phase. The docked compounds were ranked according to their predicted binding energy to the protease and a threshold of -9.0 Kcal/mol was applied resulting in a set of 2711 hits. These hits were split into different groups according to their chemical class using a rule-based classification approach. The best compound from each of the most populated classes were subjected to binding mode analysis which led to ligand-receptor interaction maps and suggested some medicinal chemistry-oriented modifications to further optimize the potency of the obtained hits. Predicted IC50 of the hit molecules ranged from 0.85 to 0.43 µM against SARS-COV2 virus Mpro protease.

scholarly journals Supercomputer-aided Drug Repositioning at Scale: Virtual Screening for SARS-CoV-2 Protease Inhibitor

2020 ◽  
Author(s):  
Sangjae Seo ◽  
Jung Woo Park ◽  
Dosik An ◽  
Junwon Yoon ◽  
Hyojung Paik ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Protease Inhibitor ◽  
Binding Affinity ◽  
High Throughput ◽  
Active Site ◽  
High Throughput Screening ◽  
Drug Repositioning ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Drug Candidates

Coronavirus diseases (COVID-19) outbreak has been labelled a pandemic. For the prioritization of treatments to cope with COVID-19, it is important to conduct rapid high-throughput screening of chemical compounds to repurposing the approved drugs, such as repositioning of chloroquine (Malaria drug) for COVID-19. In this study, exploiting supercomputer resource, we conducted high-throughput virtual screening for potential repositioning candidates of the protease inhibitor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using the three dimensional structure of main protease (Mpro) of SARS-CoV-2, we evaluated binding affinity between Mpro and drug candidates listed in SWEETLEAD library and ChEMBL database. Docking scores of 19,168 drug molecules at the active site of Mpro were calculated using Autodock Vina package. Among the calculated result, we selected 43 drug candidates and ran molecular dynamics (MD) simulation to further investigate protein-drug interaction. Among compounds that bind to the active site of SARS-CoV-2, we finally selected the 8 drugs showing the highest binding affinity; asunaprevir, atazanavir, dasabuvir, doravirine, fosamprenavir, ritonavir, voxilaprevir and amprenavir, which are the antiviral drugs of hepatitis C virus or human immunodeficiency virus. We expect that the present study provides comprehensive insights into the development of antiviral medication, especially for the treatment of COVID-19.<div><br></div><div>* Attached excel file contains a full list of results of docking calculations</div>

Reactivity-guided de novo molecular design and high throughput virtual screening of a targeted library of peptidomimetic compounds reveals charge-based structure-activity relationship of potential covalent inhibitors of the main protease of SARS-CoV-2

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Stephanie Sun ◽  
Kavya Anand ◽  
Ishani Ashok ◽  
Bhavesh Ashok ◽  
Ayush Bajaj ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Small Molecules ◽  
High Throughput ◽  
Active Site ◽  
De Novo ◽  
Cysteine Residue ◽  
Host Cells ◽  
Main Protease ◽  
Covalent Inhibitors ◽  
High Throughput Virtual Screening

In December of 2019, a novel coronavirus was first identified in Wuhan, China, and has since spread around the world, leaving a largely unsolved biomedical problem in its wake. Upon entry into host cells, the main protease is essential for the replication of viral RNA, which is what allows the virus to replicate inside humans. Inhibition of the main protease has been investigated as a potential strategy for inhibition of the viral replication cycle. Here, we designed a combinatorial library of small molecules and performed high-throughput virtual screening to identify a series of hit compounds that may serve as potential inhibitors of the main protease. In our design of covalent inhibitors of the coronavirus protease, we modeled a library of 361 peptidomimetic Michael acceptor small molecules, which are designed to engage the nucleophilic cysteine residue in the active site of the protease in an irreversible 1,4-conjugate addition. We then employed a variety of computational tools to determine the binding affinity of our designed compounds when bound to the protease active site, where we determined that cationic side chains are potentially beneficial for inhibition of SARS-CoV-2.   

scholarly journals Supercomputer-aided Drug Repositioning at Scale: Virtual Screening for SARS-CoV-2 Protease Inhibitor

2020 ◽  
Author(s):  
Sangjae Seo ◽  
Jung Woo Park ◽  
Dosik An ◽  
Junwon Yoon ◽  
Hyojung Paik ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Protease Inhibitor ◽  
Binding Affinity ◽  
High Throughput ◽  
Active Site ◽  
High Throughput Screening ◽  
Drug Repositioning ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Drug Candidates

Coronavirus diseases (COVID-19) outbreak has been labelled a pandemic. For the prioritization of treatments to cope with COVID-19, it is important to conduct rapid high-throughput screening of chemical compounds to repurposing the approved drugs, such as repositioning of chloroquine (Malaria drug) for COVID-19. In this study, exploiting supercomputer resource, we conducted high-throughput virtual screening for potential repositioning candidates of the protease inhibitor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using the three dimensional structure of main protease (Mpro) of SARS-CoV-2, we evaluated binding affinity between Mpro and drug candidates listed in SWEETLEAD library and ChEMBL database. Docking scores of 19,168 drug molecules at the active site of Mpro were calculated using Autodock Vina package. Among the calculated result, we selected 43 drug candidates and ran molecular dynamics (MD) simulation to further investigate protein-drug interaction. Among compounds that bind to the active site of SARS-CoV-2, we finally selected the 8 drugs showing the highest binding affinity; asunaprevir, atazanavir, dasabuvir, doravirine, fosamprenavir, ritonavir, voxilaprevir and amprenavir, which are the antiviral drugs of hepatitis C virus or human immunodeficiency virus. We expect that the present study provides comprehensive insights into the development of antiviral medication, especially for the treatment of COVID-19.<div><br></div><div>* Attached excel file contains a full list of results of docking calculations</div>

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.

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