scholarly journals Screening of Natural Product and Natural Product like Molecules against SARS–CoV–2 Main Protease Using Molecular Modeling Methods

2021 ◽  
pp. 36-51
Author(s):  
Ismail Hakki Akgün
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
Natural Product ◽  
Enzyme Inhibitors ◽  
Three Dimensional ◽  
Screening Methods ◽  
Data Set ◽  
Modeling Methods ◽  
Main Protease ◽  
Synthetic Accessibility

Objective: To determine possible MPro enzyme inhibitors by using structure-based virtual screening methods, in the ZINC Biogenic Data Set containing natural products and natural product-like molecules. Materials and Methods: QVina, an AutoDockVina derivative, was used in virtual screening operations, GROMACS in molecular dynamics studies and SwissAdme server in ADME (Absorption, Distribution, Metabolism, and Excretion) calculations. KNIME (Konstanz Information Miner) and ChemAxon software were used for filtering data and creating three-dimensional structures of the molecules. Results: Seven out of totally screened 51535 natural products or natural products like molecules were identified as possible candidate to be used as SARS–CoV–2 Main Protease (MPro) enzyme inhibitors based on the results obtained from structure based virtual screening and ADME models. Conclusion: Among the seven potent molecules, two of them (ZINC000604382012 and ZINC000514288074) were selected as candidate molecules for further studies according to the results obtained from g_mmpbsa simulations and synthetic accessibility models. In addition, a workflow has been established to identify novel or potent Mpro enzyme inhibitors.

scholarly journals High Performance Computing Prediction of Potential Natural Product Inhibitors of SARS-CoV-2 Key Targets

2020 ◽  
Author(s):  
Kendall Byler ◽  
Joseph Landman ◽  
Jerome Baudry
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
High Performance Computing ◽  
Natural Product ◽  
High Performance ◽  
Spike Protein ◽  
Main Protease ◽  
The Common ◽  
Performance Computing

This work describes using a supercomputer to perform virtual screening of natural products and natural products derivatives against several conformations of 3 proteins of SARS-CoC-2 : papain-like protease, main protease and spike protein. We analyze the common chemical features of the top molecules predicted to bind and describe the pharmacophores responsible for the predicted binding.

scholarly journals High Performance Computing Prediction of Potential Natural Product Inhibitors of SARS-CoV-2 Key Targets

2020 ◽  
Author(s):  
Kendall Byler ◽  
Joseph Landman ◽  
Jerome Baudry
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
High Performance Computing ◽  
Natural Product ◽  
High Performance ◽  
Spike Protein ◽  
Main Protease ◽  
The Common ◽  
The University ◽  
Performance Computing

This work describes using a supercomputer to perform virtual screening of natural products and natural products derivatives against several conformations of 3 proteins of SARS-CoC-2 : papain-like protease, main protease and spike protein. We analyze the common chemical features of the top molecules predicted to bind and describe the pharmacophores responsible for The University of Ala predicted binding.

scholarly journals High Performance Computing Prediction of Potential Natural Product Inhibitors of SARS-CoV-2 Key Targets

2020 ◽  
Author(s):  
Kendall Byler ◽  
Joseph Landman ◽  
Jerome Baudry
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
High Performance Computing ◽  
Natural Product ◽  
High Performance ◽  
Spike Protein ◽  
Main Protease ◽  
The Common ◽  
Performance Computing

This work describes using a supercomputer to perform virtual screening of natural products and natural products derivatives against several conformations of 3 proteins of SARS-CoC-2 : papain-like protease, main protease and spike protein. We analyze the common chemical features of the top molecules predicted to bind and describe the pharmacophores responsible for the predicted binding.

scholarly journals Natural products as inhibitors of COVID-19 main protease – A virtual screening by molecular docking

2020 ◽  
Author(s):  
Marzieh omrani ◽  
Mohammad Bayati ◽  
Parvaneh Mehrbod ◽  
Samad Nejad-Ebrahimi
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
High Throughput ◽  
Herbal Medicines ◽  
Respiratory Illness ◽  
Fast Method ◽  
Main Protease ◽  
Glide Docking ◽  
Adme Properties ◽  
High Throughput Virtual Screening

Abstract Background: The novel coronavirus (2019-nCoV) causes a severe respiratory illness that was unknown in the human before. Its alarmingly quick transmission to many countries across the world resulted in a worldwide health emergency. It has caused a notable percentage of morbidity and mortality. Therefore, an imminent need for drugs to combat this disease has been increased. Global collaborative efforts from scientists are underway to find a therapy to treat infections and reduce death cases. Herbal medicines and purified natural products have been reported to have antiviral activity against Coronaviruses (CoVs).Methods: In this study, a High Throughput Virtual Screening (HTVS) protocol was used as a fast method on the discovery of novel drug candidates as the COVID-19 main protease inhibitors. Over 180,000 natural product-based compounds were obtained from the ZINC database and virtually screened against the COVID-19 main protease. In this study, the Glide docking program was applied for high throughput virtual screening. Extra precision (XP) and in a combination of Prime module, induced-fit docking (IFD) approach was also used. Additionally, the ADME properties of all compounds were analyzed, and the final selection was carried out based on the Lipinski rule of five. Results: The nineteen compounds were selected and introduced as new potential inhibitors. The compound ZINC08765174 (1-[3-(1H-indol-3-yl) propanoyl]-N-(4-phenylbutan-2-yl)piperidine-3-carboxamide) showed a strong binding affinity (-11.5 kcal/mol) to the crucial residues of COVID-19 main protease comparing to peramivir (-9.8 kcal/mol) as a positive control.Conclusions: The excellent ADME properties proposed the opportunity of this compound to be a promising candidate for the treatment of COVID-19.

A primer on natural product-based virtual screening

2019 ◽  
Vol 4 (6) ◽  
Author(s):  
Eleni Koulouridi ◽  
Marilia Valli ◽  
Fidele Ntie-Kang ◽  
Vanderlan da Silva Bolzani
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Molecular Modeling ◽  
Virtual Screening ◽  
Natural Product ◽  
General Purpose ◽  
Target Type ◽  
Computational Techniques ◽  
Specific Subset ◽  
Virtual Libraries

Abstract Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.

Single Crystal X-Ray Structural Determination: A Powerful Technique for Natural Products Research and Drug Discovery

2012 ◽  
Vol 545 ◽  
pp. 3-15
Author(s):  
Hoong Kun Fun ◽  
Suchada Chantrapromma ◽  
Nawong Boonnak
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Single Crystal ◽  
Natural Product ◽  
Structure Determination ◽  
Three Dimensional ◽  
X Ray ◽  
Powerful Technique ◽  
Determination Of Absolute Configuration

Drug discovery from natural products resources have been extensively studied. The most important step in the discovery process is the identification of compounds with interesting biological activity. Single crystal X-ray structure determination is a powerful technique for natural products research and drug discovery in which the detailed three-dimensional structures that emerge can be co-related to the activities of these structures. This article shall present (i) co-crystal structures, (ii) determination of absolute configuration and (iii) the ability to distinguish between whether a natural product compound is a natural product or a natural product artifact. All these three properties are unique to the technique of single crystal X-ray structure determination.

scholarly journals Natural Product Scores and Fingerprints Extracted from Artificial Neural Networks.

2021 ◽  
Author(s):  
Janosch Menke ◽  
Joana Massa ◽  
Oliver Koch
Keyword(s):  
Neural Networks ◽  
Natural Products ◽  
Artificial Neural Networks ◽  
Virtual Screening ◽  
Natural Product ◽  
Recent Work ◽  
The Novel ◽  
Multi Layer Perceptron ◽  
Output Layer ◽  
Depth Analysis

<div>Due to its desirable properties, natural products are an important ligand class for medicinal chemists. However, due to their structural distinctiveness, traditional cheminformatic approaches, like ligand-based virtual screening, often perform worse for natural products. Based on our recent work, we evaluated the ability of neural networks to generate fingerprints more appropriate for the use with natural products. A manually curated dataset of natural products and synthetic decoys was used to train a multi-layer perceptron network and an autoencoder-like network. An in-depth analysis showed that the extracted natural product specific neural fingerprints outperforms traditional as well as natural product specific fingerprints on three datasets. Further, we explore how the activation from the output layer of a network can work as a novel natural product likeness score. Overall two natural product specific datasets were generated, which are publicly available together with the code to create the fingerprints and the novel natural product likeness score.<br></div>

scholarly journals Natural products as inhibitors of COVID-19 main protease–A virtual screening by molecular docking

2021 ◽  
Author(s):  
Marzieh Omrani ◽  
Mohammad Bayati ◽  
Parvaneh Mehrbod ◽  
Kamal Asmari Bardazard ◽  
Samad Nejad-Ebrahimi
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
High Throughput ◽  
Herbal Medicines ◽  
Respiratory Illness ◽  
Fast Method ◽  
Main Protease ◽  
Glide Docking ◽  
Adme Properties ◽  
High Throughput Virtual Screening

Background: The novel coronavirus (2019-nCoV) causes a severe respiratory illness unknown to a human before. Its alarmingly quick transmission to many countries across the world has resulted in a global health emergency. Therefore, an imminent need for drugs to combat this disease has been increased. Worldwide collaborative efforts from scientists are underway to determine a therapy to treat COVID-19 infections and reduce mortality rates. Since herbal medicines and purified natural products have been reported to have antiviral activity against Coronaviruses (CoVs), this in silico evaluation was performed for identifying potential natural compounds with promising inhibitory activities against COVID-19. Methods: In this study, a High Throughput Virtual Screening (HTVS) protocol was used as a fast method for discovering novel drug candidates as potential COVID-19 main protease (Mpro) inhibitors. Over 180,000 natural product-based compounds were obtained from the ZINC database and virtually screened against the COVID-19 Mpro. In this study, the Glide docking program was applied for high throughput virtual screening. Also, Extra precision (XP) has been used following the induced-fit docking (IFD) approach. The ADME properties of all compounds were analyzed and a final selection was made based on the Lipinski rule of five. Also, molecular dynamics (MD) simulations were conducted for a virtual complex of the best scoring compound with COVID-19 protease. Results: Nineteen compounds were introduced as new potential inhibitors. Compound ZINC08765174 (1-[3-(1H-indol-3-yl) propanoyl]-N-(4-phenylbutan-2-yl)piperidine-3-carboxamide showed a strong binding affinity (-11.5 kcal/mol) to the COVID-19 Mpro comparing to peramivir (-9.8 kcal/mol) as a positive control. Conclusions: Based on these findings, nineteen compounds were proposed as possible new COVID-19 inhibitors, of which ZINC08765174 had a high affinity to Mpro. Furthermore, the promising ADME properties of the selected compounds emphasize their potential as attractive candidates for the treatments of COVID-19.

scholarly journals Structure-Based Virtual Screening of a Natural Product Database to Identify Several Possible SARS-CoV-2 Main Protease Inhibitors

2020 ◽  
Author(s):  
Azhagiya Singam Ettayapuram Ramaprasad ◽  
Michele La merrill ◽  
Kathleen A. Durkin ◽  
Martyn T. Smith
Keyword(s):  
Natural Products ◽  
Natural Product ◽  
Respiratory Illness ◽  
Dynamics Simulation ◽  
Effective Vaccine ◽  
Main Protease ◽  
Novel Coronavirus ◽  
Seriously Ill Patients ◽  
Enormous Number ◽  
Seriously Ill

<p>A novel coronavirus (SARS-CoV-2) has been the cause of a recent pandemic of respiratory illness known as COVID-19. The lack of anti-viral drugs or vaccines to control the infection has resulted in an enormous number of seriously ill patients requiring hospitalization. In the absence of an effective vaccine, there is an urgent need for therapies which can fight COVID-19 infection. Readily available compounds in foods and plants may be one source of anti-viral compounds. Here, natural product chemicals from the Nuclei of Bioassays, Ecophysiology and Biosynthesis of Natural Products Database (NuBBE<sub>DB</sub>) were screened against the main protease (Mpro) of SARS-CoV-2. This protease was chosen as a target due to its importance in the replication of SARS-CoV-2. Molecular docking was used to screen the natural products against Mpro to identify potential candidates. The identified candidates were further filtered using molecular dynamics simulation investigation. Nine natural compounds were identified for experimental validation, with carlinoside and quercetin 3-o-sophoroside being the top candidates. </p>

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.

Sign in / Sign up

Export Citation Format

Share Document