Virtual Screening of the Flavonoids Compounds with the SARS-CoV-2 3C-like Protease as the Lead Compounds for the COVID-19

Coronaviruses ◽  
2021 ◽  
Vol 02 ◽  
Author(s):  
Gabriella Patricia Adisurja ◽  
Arli Aditya Parkesit
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
Specific Binding ◽  
Docking Simulation ◽  
Screening Methods ◽  
Lead Compound ◽  
Qsar Analysis ◽  
Lead Compounds ◽  
Protease Enzyme ◽  
Lead Source

: As per the1st of September 2020, the COVID-19 pandemic has reached an unprecedented level of more than 25 million cases with more than 850,000 deaths. Moreover, all the drug candidates are still undergoing testing in clinical trial. In this regard, a breakthrough in drug design is necessary. One strategy to devise lead compounds is leveraging natural products as a lead source. Several companies and research institutes are currently developing anti-SARS-CoV-2 leads from natural products. Flavanoids are well known as a class of antiviral compounds library. The objective of this research is to employ virtual screening methods for obtaining the best lead compounds from the library of flavonoid compounds. This research employed virtual screening methods that comprised of downloading the protein and lead compound structures, QSAR analysis prediction, iterations of molecular docking simulation, and ADME-TOX simulation for toxicity prediction. The QSAR analysis found that the tested compounds have broad-spectrum antiviral activity, and some of them exhibit specific binding to the 3C-like Protease of the Coronavirus. Moreover, juglanin was found as the compound with the most fit binding with the Protease enzyme of SARS-CoV-2. Although most of the tested compounds are deemed toxic by the ADME-Tox test, further research should be conducted to comprehend the most feasible strategy to deliver the drug to the infected lung cells. The juglanin compound is selected as the most fit candidate as the SARS-CoV-2 lead compound in the tested flavonoid samples. However, further research should be conducted to observe the lead delivery method to the cell.

scholarly journals Structure-based virtual screening for new lead compounds targeted Plasmodium α-tubulin

2021 ◽  
Vol 28 ◽  
pp. 135-139
Author(s):  
O. V. Rayevsky ◽  
O. M. Demchyk ◽  
P. A. Karpov ◽  
S. P. Ozheredov ◽  
S. I. Spivak ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Protein Interaction ◽  
Specific Binding ◽  
Pharmacophore Model ◽  
Computational Prediction ◽  
Mitotic Apparatus ◽  
Ligand Interaction ◽  
Lead Compounds ◽  
Key Functional Groups ◽  
Potential Inhibitors

Aim. Search for new dinitroaniline and phosphorothioamide compounds, capable of selective binding with Plasmodium α-tubulin, affecting its mitotic apparatus. Methods. Structural biology methods of computational prediction of protein-ligand interaction: molecular docking, molecular dynamics and pharmacophore analysis. Selection of compounds based on pharmacophore characteristics and virtual screening results. Results. The protocol and required structural conditions for target (α-tubulin of P. falciparum) preparation and correct modeling of the ligand-protein interaction (docking and virtual screening) were developed. The generalized pharmacophore model of ligand-protein interaction and key functional groups of ligands responsible for specific binding were identified. Conclusions. Based on results of virtual screening, 22 commercial compounds were selected. Identified compounds proposed as potential inhibitors of Plasmodium mitotic machinery and the base of new antimalarial drugs. Keywords: malaria, Plasmodium, intermolecular interaction, dinitroaniline derived, phosphorothioamidate derived.

scholarly journals In SilicoInvestigation of Potential Pyruvate Kinase M2 Regulators from Traditional Chinese Medicine against Cancers

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Kuan-Chung Chen ◽  
Kuen-Bao Chen ◽  
Hsin-Yi Chen ◽  
Calvin Yu-Chian Chen
Keyword(s):  
Virtual Screening ◽  
Drug Development ◽  
Pyruvate Kinase ◽  
Md Simulation ◽  
Docking Simulation ◽  
Target Protein ◽  
Binding Affinities ◽  
Pyruvate Kinase M2 ◽  
Lead Compounds ◽  
The Stability

A recent research in cancer research demonstrates that tumor-specific pyruvate kinase M2 (PKM2) plays an important role in chromosome segregation and mitosis progression of tumor cells. To improve the drug development of TCM compounds, we aim to identify potent TCM compounds as lead compounds of PKM2 regulators. PONDR-Fit protocol was utilized to predict the disordered disposition in the binding domain of PKM2 protein before virtual screening as the disordered structure in the protein may cause the side effect and downregulation of the possibility of ligand to bind with target protein. MD simulation was performed to validate the stability of interactions between PKM2 proteins and each ligand after virtual screening. The top TCM compounds, saussureamine C and precatorine, extracted fromLycium chinenseMill. andAbrus precatoriusL., respectively, have higher binding affinities with target protein in docking simulation than control. They have stable H-bonds with residues A:Lys311 and some other residues in both chains of PKM2 protein. Hence, we propose the TCM compounds, saussureamine C and precatorine, as potential candidates as lead compounds for further study in drug development process with the PKM2 protein against cancer.

scholarly journals In SilicoInvestigation of Potential mTOR Inhibitors from Traditional Chinese Medicine for Treatment of Leigh Syndrome

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Kuan-Chung Chen ◽  
Wen-Yuan Lee ◽  
Hsin-Yi Chen ◽  
Calvin Yu-Chian Chen
Keyword(s):  
Virtual Screening ◽  
Md Simulation ◽  
Mammalian Target Of Rapamycin ◽  
Docking Simulation ◽  
Leigh Syndrome ◽  
Mtor Inhibitors ◽  
Mitochondrial Disorders ◽  
Lead Compounds ◽  
Mtor Protein ◽  
The Stability

A recent research demonstrates that the inhibition of mammalian target of rapamycin (mTOR) improves survival and health for patients with Leigh syndrome. mTOR proteins can be treated as drug target proteins against Leigh syndrome and other mitochondrial disorders. In this study, we aim to identify potent TCM compounds from the TCM Database@Taiwan as lead compounds of mTOR inhibitors. PONDR-Fit protocol was employed to predict the disordered disposition in mTOR protein before virtual screening. After virtual screening, the MD simulation was employed to validate the stability of interactions between each ligand and mTOR protein in the docking poses from docking simulation. The top TCM compounds, picrasidine M and acerosin, have higher binding affinities with target protein in docking simulation than control. There have H-bonds with residues Val2240 andπinteractions with common residue Trp2239. After MD simulation, the top TCM compounds maintain similar docking poses under dynamic conditions. The top two TCM compounds, picrasidine M and acerosin, were extracted fromPicrasma quassioides(D. Don) Benn. andVitex negundoL. Hence, we propose the TCM compounds, picrasidine M and acerosin, as potential candidates as lead compounds for further study in drug development process with the mTOR protein against Leigh syndrome and other mitochondrial disorders.

scholarly journals ABBV-744 as a potential inhibitor of SARS-CoV-2 main protease enzyme against COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zeynab Fakhar ◽  
Shama Khan ◽  
Suliman Y. AlOmar ◽  
Afrah Alkhuriji ◽  
Aijaz Ahmad
Keyword(s):  
Virtual Screening ◽  
Drug Repurposing ◽  
Pharmacophore Modeling ◽  
Binding Energies ◽  
Active Inhibitor ◽  
Lead Compounds ◽  
Protease Enzyme ◽  
Main Protease ◽  
Dynamics Simulations ◽  
Potential Inhibitors

AbstractA new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and become pandemic with thousands new deaths and infected cases globally. To address coronavirus disease (COVID-19), currently no effective drug or vaccine is available. This necessity motivated us to explore potential lead compounds by considering drug repurposing approach targeting main protease (Mpro) enzyme of SARS-CoV-2. This enzyme considered to be an attractive drug target as it contributes significantly in mediating viral replication and transcription. Herein, comprehensive computational investigations were performed to identify potential inhibitors of SARS-CoV-2 Mpro enzyme. The structure-based pharmacophore modeling was developed based on the co-crystallized structure of the enzyme with its biological active inhibitor. The generated hypotheses were applied for virtual screening based PhaseScore. Docking based virtual screening workflow was used to generate hit compounds using HTVS, SP and XP based Glide GScore. The pharmacological and physicochemical properties of the selected lead compounds were characterized using ADMET. Molecular dynamics simulations were performed to explore the binding affinities of the considered lead compounds. Binding energies revealed that compound ABBV-744 binds to the Mpro with strong affinity (ΔGbind −45.43 kcal/mol), and the complex is more stable in comparison with other protein–ligand complexes. Our study classified three best compounds which could be considered as promising inhibitors against main protease SARS-CoV-2 virus.

scholarly journals ABBV-744 and Onalespid as Potential Inhibitors of SARS-CoV-2 main Protease Enzyme: A Promising Therapeutics in COVID-19?

2020 ◽  
Author(s):  
Zeynab Fakhar ◽  
Shama Khan ◽  
Aijaz Ahmad
Keyword(s):  
Virtual Screening ◽  
Drug Repurposing ◽  
Pharmacophore Modeling ◽  
Binding Studies ◽  
Active Inhibitor ◽  
Lead Compounds ◽  
Protease Enzyme ◽  
Main Protease ◽  
Dynamics Simulations ◽  
Potential Inhibitors

Abstract A new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and become pandemic with thousands new deaths and infected cases globally. To address coronavirus disease (COVID-19), currently no effective drug or vaccine is available. This necessity motivated us to explore potential lead compounds by considering drug repurposing approach targeting main protease (Mpro) enzyme of SARS-CoV-2. This enzyme considered to be an attractive drug target as it contributes significantly in mediating viral replication and transcription. Herein, comprehensive computational investigations were performed to identify potential inhibitors of SARS-CoV-2 Mpro enzyme. The structure-based pharmacophore modeling was developed based on the co-crystallized structure of the enzyme with its biological active inhibitor. The generated hypotheses were applied for virtual screening based PhaseScore. Docking based virtual screening work-flow was used to generate hit compounds using HTVS, SP and XP based Glide GScore. The pharmacological and physicochemical properties of the best hit compounds were characterized using ADMET. Molecular dynamics simulations were performed to explore the binding affinities of the considered compounds. Binding studies revealed that compound ABBV-744 binds to the Mpro with strong affinity (Gbind -45.43 kcal/mol), and the complex is more stable in comparison with other protein-ligand complexes. Our study classified three best compounds which could be considered as promising inhibitors against main protease SARS-CoV-2 virus.

Natural Products of Dietary Origin as Lead Compounds in Virtual Screening and Drug Design

2012 ◽  
Vol 13 (1) ◽  
pp. 117-124 ◽  
Author(s):  
Werner J. Geldenhuys ◽  
Anupam Bishayee ◽  
Altaf S. Darvesh ◽  
Richard T. Carroll
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
Drug Design ◽  
Lead Compounds ◽  
Dietary Origin

scholarly journals In SilicoInvestigation of Potential TRAF6 Inhibitor from Traditional Chinese Medicine against Cancers

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Kuan-Chung Chen ◽  
Wen-Yuan Lee ◽  
Hsin-Yi Chen ◽  
Calvin Yu-Chian Chen
Keyword(s):  
Virtual Screening ◽  
Bos Taurus ◽  
Dynamic Condition ◽  
Hypoxia Inducible Factor ◽  
Tumor Necrosis Factor Receptor ◽  
Docking Simulation ◽  
Target Protein ◽  
Lead Compounds ◽  
Hypoxia Inducible Factor 1 ◽  
The Stability

It has been indicated that tumor necrosis factor receptor-associated factor-6 (TRAF6) will upregulate the expression of hypoxia-inducible factor-1α(HIF-1α) and promote tumor angiogenesis. TRAF6 proteins can be treated as drug target proteins for a differentiation therapy against cancers. As structural disordered disposition in the protein may induce the side-effect and reduce the occupancy for ligand to bind with target protein, PONDR-Fit protocol was performed to predict the disordered disposition in TRAF6 protein before virtual screening. TCM compounds from the TCM Database@Taiwan were employed for virtual screening to identify potent compounds as lead compounds of TRAF6 inhibitor. After virtual screening, the MD simulation was performed to validate the stability of interactions between TRAF6 proteins and each ligand. The top TCM compounds, tryptophan, diiodotyrosine, and saussureamine C, extracted fromSaussurea lappaClarke,Bos taurus domesticusGmelin, andLycium chinenseMill., have higher binding affinities with target protein in docking simulation. However, the docking pose of TRAF6 protein with tryptophan is not stable under dynamic condition. For the other two TCM candidates, diiodotyrosine and saussureamine C maintain the similar docking poses under dynamic conditions. Hence, we propose the TCM compounds, diiodotyrosine and saussureamine C, as potential candidates as lead compounds for further study in drug development process with the TRAF6 protein against cancer.

Convolutional Neural Network Based Virtual Screening

2020 ◽  
Vol 27 ◽  
Author(s):  
Wenying Shan ◽  
Xuanyi Li ◽  
Hequan Yao ◽  
Kejiang Lin
Keyword(s):  
Neural Networks ◽  
Virtual Screening ◽  
Scoring Function ◽  
Learning Ability ◽  
Screening Methods ◽  
Scoring Functions ◽  
Lead Compound ◽  
Chemical Screening ◽  
Powerful Learning ◽  
Important Means

: Virtual screening is an important means for lead compound discovery. The scoring function is the key to selecting hit compounds. Many scoring functions are currently available; however, there are no all-purpose scoring functions because different scoring functions tend to have conflicting results. Recently, neural networks, especially convolutional neural networks, have been constantly penetrating drug design and most CNN-based virtual screening methods are superior to traditional docking methods, such as Dock and AutoDock. CNN-based virtual screening is expected to improve the previous model of overreliance on computational chemical screening. Utilizing the powerful learning ability of neural networks provides us with a new method for evaluating compounds. We review the latest progress of CNN-based virtual screening and propose prospects.

Forging New Scaffolds from Old: Combining Scaffold Hopping and Hierarchical Virtual Screening for Identifying Novel Bcl-2 Inhibitors

2019 ◽  
Vol 19 (13) ◽  
pp. 1162-1172 ◽  
Author(s):  
Vishnupriya Kanakaveti ◽  
Sakthivel Rathinasamy ◽  
Suresh K. Rayala ◽  
Michael Gromiha
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Physicochemical Characterization ◽  
B Cell Lymphoma ◽  
Rational Approach ◽  
Screening Methods ◽  
Scaffold Hopping ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Lead Compounds

Background: Though virtual screening methods have proven to be potent in various instances, the technique is practically incomplete to quench the need of drug discovery process. Thus, the quest for novel designing approaches and chemotypes for improved efficacy of lead compounds has been intensified and logistic approaches such as scaffold hopping and hierarchical virtual screening methods were evolved. Till now, in all the previous attempts these two approaches were applied separately. Objective: In the current work, we made a novel attempt in terms of blending scaffold hopping and hierarchical virtual screening. The prime objective is to assess the hybrid method for its efficacy in identifying active lead molecules for emerging PPI target Bcl-2 (B-cell Lymphoma 2). Method: We designed novel scaffolds from the reported cores and screened a set of 8270 compounds using both scaffold hopping and hierarchical virtual screening for Bcl-2 protein. Also, we enumerated the libraries using clustering, PAINS filtering, physicochemical characterization and SAR matching. Results: We generated a focused library of compounds towards Bcl-2 interface, screened the 8270 compounds and identified top hits for seven families upon fine filtering with PAINS algorithm, features, SAR mapping, synthetic accessibility and similarity search. Our approach retrieved a set of 50 lead compounds. Conclusions: Finding rational approach meeting the needs of drug discovery process for PPI targets is the need of the hour which can be fulfilled by an extended scaffold hopping approach resulting in focused PPI targeting by providing novel leads with better potency.

scholarly journals Identification of Potent Chloride Intracellular Channel Protein 1 Inhibitors from Traditional Chinese Medicine through Structure-Based Virtual Screening and Molecular Dynamics Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Wei Wang ◽  
Minghui Wan ◽  
Dongjiang Liao ◽  
Guilin Peng ◽  
Xin Xu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Natural Products ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Virtual Screening ◽  
Md Simulation ◽  
Interaction Analysis ◽  
Docking Simulation ◽  
Building Blocks ◽  
Intracellular Channel

Chloride intracellular channel 1 (CLIC1) is involved in the development of most aggressive human tumors, including gastric, colon, lung, liver, and glioblastoma cancers. It has become an attractive new therapeutic target for several types of cancer. In this work, we aim to identify natural products as potent CLIC1 inhibitors from Traditional Chinese Medicine (TCM) database using structure-based virtual screening and molecular dynamics (MD) simulation. First, structure-based docking was employed to screen the refined TCM database and the top 500 TCM compounds were obtained and reranked by X-Score. Then, 30 potent hits were achieved from the top 500 TCM compounds using cluster and ligand-protein interaction analysis. Finally, MD simulation was employed to validate the stability of interactions between each hit and CLIC1 protein from docking simulation, and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) analysis was used to refine the virtual hits. Six TCM compounds with top MM-GBSA scores and ideal-binding models were confirmed as the final hits. Our study provides information about the interaction between TCM compounds and CLIC1 protein, which may be helpful for further experimental investigations. In addition, the top 6 natural products structural scaffolds could serve as building blocks in designing drug-like molecules for CLIC1 inhibition.

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