Browse
Submit Data
Databases
API
Help

Dataset Information

29 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Identification of Food Compounds as inhibitors of SARS-CoV-2 main protease using molecular docking and molecular dynamics simulations.

ABSTRACT: SARS-CoV-2 has rapidly emerged as a global pandemic with high infection rate. At present, there is no drug available for this deadly disease. Recently, M^pro (Main Protease) enzyme has been identified as essential proteins for the survival of this virus. In the present work, Lipinski's rules and molecular docking have been performed to identify plausible inhibitors of M^pro using food compounds. For virtual screening, a database of food compounds was downloaded and then filtered using Lipinski's rule of five. Then, molecular docking was accomplished to identify hits using M^pro protein as the target enzyme. This led to identification of a Spermidine derivative as a hit. In the next step, Spermidine derivatives were collected from PubMed and screened for their binding with M^pro protein. In addition, molecular dynamic simulations (200 ns) were executed to get additional information. Some of the compounds are found to have strong affinity for M^pro, therefore these hits could be used to develop a therapeutic agent for SARS-CoV-2.

SUBMITTER: Masand VH

PROVIDER: S-EPMC8295492 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Identification of natural compounds as potent inhibitors of SARS-CoV-2 main protease using combined docking and molecular dynamics simulations.

Project description:Coronavirus disease 2019 (COVID-19) has emerged from China and globally affected the entire population through the human-to-human transmission of a newly emerged virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The genome of SARS-CoV-2 encodes several proteins that are essential for multiplication and pathogenesis. The main protease (Mpro or 3CLpro) of SARS-CoV-2 plays a central role in its pathogenesis and thus is considered as an attractive drug target for the drug design and development of small-molecule inhibitors. We have employed an extensive structure-based high-throughput virtual screening to discover potential natural compounds from the ZINC database which could inhibit the Mpro of SARS-CoV-2. Initially, the hits were selected on the basis of their physicochemical and drug-like properties. Subsequently, the PAINS filter, estimation of binding affinities using molecular docking, and interaction analyses were performed to find safe and potential inhibitors of SARS-CoV-2 Mpro. We have identified ZINC02123811 (1-(3-(2,5,9-trimethyl-7-oxo-3-phenyl-7H-furo[3,2-g]chromen-6-yl)propanoyl)piperidine-4-carboxamide), a natural compound bearing appreciable affinity, efficiency, and specificity towards the binding pocket of SARS-CoV-2 Mpro. The identified compound showed a set of drug-like properties and preferentially binds to the active site of SARS-CoV-2 Mpro. All-atom molecular dynamics (MD) simulations were performed to evaluate the conformational dynamics, stability and interaction mechanism of Mpro with ZINC02123811. MD simulation results indicated that Mpro with ZINC02123811 forms a stable complex throughout the trajectory of 100 ns. These findings suggest that ZINC02123811 may be further exploited as a promising scaffold for the development of potential inhibitors of SARS-CoV-2 Mpro to address COVID-19.

| S-EPMC7839507 | biostudies-literature

Molecular docking, validation, dynamics simulations, and pharmacokinetic prediction of natural compounds against the SARS-CoV-2 main-protease.

Project description:The study aims to evaluate the potency of two hundred natural antiviral phytocompounds against the active site of the Severe Acquired Respiratory Syndrome - Coronavirus - 2 (SARS-CoV-2) Main-Protease (Mpro) using AutoDock 4.2.6. The three- dimensional crystal structure of the Mpro (PDB Id: 6LU7) was retrieved from the Protein Data Bank (PDB), the active site was predicted using MetaPocket 2.0. Food and Drug Administration (FDA) approved viral protease inhibitors were used as standards for comparison of results. The compounds theaflavin-3-3'-digallate, rutin, hypericin, robustaflavone, and (-)-solenolide A with respective binding energy of -12.41 (Ki = 794.96 pM); -11.33 (Ki = 4.98 nM); -11.17 (Ki = 6.54 nM); -10.92 (Ki = 9.85 nM); and -10.82 kcal/mol (Ki = 11.88 nM) were ranked top as Coronavirus Disease - 2019 (COVID-19) Mpro inhibitors. The interacting amino acid residues were visualized using Discovery Studio 3.5 to elucidate the 2-dimensional and 3-dimensional interactions. The study was validated by i) re-docking the N3-peptide inhibitor-Mpro and superimposing them onto co-crystallized complex and ii) docking decoy ligands to Mpro. The ligands that showed low binding energy were further predicted for and pharmacokinetic properties and Lipinski's rule of 5 and the results are tabulated and discussed. Molecular dynamics simulations were performed for 50 ns for those compounds using the Desmond package, Schrödinger to assess the conformational stability and fluctuations of protein-ligand complexes during the simulation. Thus, the natural compounds could act as a lead for the COVID-19 regimen after in-vitro and in- vivo clinical trials.Communicated by Ramaswamy H. Sarma.

| S-EPMC7573242 | biostudies-literature

Design of Potent Inhibitors Targeting the Main Protease of SARS-CoV-2 Using QSAR Modeling, Molecular Docking, and Molecular Dynamics Simulations.

Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a serious global public health threat. The evolving strains of SARS-CoV-2 have reduced the effectiveness of vaccines. Therefore, antiviral drugs against SARS-CoV-2 are urgently needed. The main protease (Mpro) of SARS-CoV-2 is an extremely potent target due to its pivotal role in virus replication and low susceptibility to mutation. In the present study, a quantitative structure-activity relationship (QSAR) study was performed to design new molecules that might have higher inhibitory activity against SARS-CoV-2 Mpro. In this context, a set of 55 dihydrophenanthrene derivatives was used to build two 2D-QSAR models using the Monte Carlo optimization method and the Genetic Algorithm Multi-Linear Regression (GA-MLR) method. From the CORAL QSAR model outputs, the promoters responsible for the increase/decrease in inhibitory activity were extracted and interpreted. The promoters responsible for an increase in activity were added to the lead compound to design new molecules. The GA-MLR QSAR model was used to ensure the inhibitory activity of the designed molecules. For further validation, the designed molecules were subjected to molecular docking analysis and molecular dynamics simulations along with an absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. The results of this study suggest that the newly designed molecules have the potential to be developed as effective drugs against SARS-CoV-2.

| S-EPMC10146715 | biostudies-literature

Molecular dynamics simulation of docking structures of SARS-CoV-2 main protease and HIV protease inhibitors.

Project description:In this paper we investigate 10 different HIV protease inhibitors (HPIs) as possible repurposed-drugs candidates against SARS-CoV-2. To this end, we execute molecular docking and molecular dynamics simulations. The in silico data demonstrated that, despite their molecular differences, all HPIs presented a similar behavior for the parameters analyzed, with the exception of Nelfinavir that showed better results for most of the molecular dynamics parameters in comparison with the N3 inhibitor.

| S-EPMC7443253 | biostudies-literature

Hunting the main protease of SARS-CoV-2 by plitidepsin: Molecular docking and temperature-dependent molecular dynamics simulations.

Project description:COVID-19 has shaken all the countries across the globe and researchers are trying to find promising antiviral to cure the patients suffering from infection and can decrease the death. Even, different nations are using repurposing drugs to cure the symptoms and these repurposing drugs are hydroxychloroquine, remdesivir, and lopinavir, and recently, India has recently given the approval for the 2-deoxy-D-glucose for emergency purpose to cure the patients suffering from the COVID-19. Plitidepsin is a popular molecule and can be used in treatment of myeloma. Plitidepsin was explored by scientists experimentally against the COVID-19 and was given to the patient. It is found to be more a promising repurposing drug against the COVID-19 than the remdesivir. Therefore, there is a need to understand the interaction of plitidepsin with the main protease of SARS-CoV-2. Molecular docking of the plitidepsin against Mpro of SARS-CoV-2 was performed and the binding energy was found to be - 137.992 kcal/mol. Furthermore, authors have performed the molecular dynamics simulations of the main protease of SARS-CoV-2 in presence of plitidepsin at 300 and 325 K. It was found that the plitidepsin binds effectively with the main protease of SARS-CoV-2 at 300 K.

| S-EPMC8607790 | biostudies-literature

Evaluation of flavonoids as potential inhibitors of the SARS-CoV-2 main protease and spike RBD: Molecular docking, ADMET evaluation and molecular dynamics simulations

Project description: Not available

| S-EPMC9428111 | biostudies-literature

Phytochemicals from Leucas zeylanica Targeting Main Protease of SARS-CoV-2: Chemical Profiles, Molecular Docking, and Molecular Dynamics Simulations.

Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a contemporary coronavirus, has impacted global economic activity and has a high transmission rate. As a result of the virus's severe medical effects, developing effective vaccinations is vital. Plant-derived metabolites have been discovered as potential SARS-CoV-2 inhibitors. The SARS-CoV-2 main protease (Mpro) is a target for therapeutic research because of its highly conserved protein sequence. Gas chromatography-mass spectrometry (GC-MS) and molecular docking were used to screen 34 compounds identified from Leucas zeylanica for potential inhibitory activity against the SARS-CoV-2 Mpro. In addition, prime molecular mechanics-generalized Born surface area (MM-GBSA) was used to screen the compound dataset using a molecular dynamics simulation. From molecular docking analysis, 26 compounds were capable of interaction with the SARS-CoV-2 Mpro, while three compounds, namely 11-oxa-dispiro[4.0.4.1]undecan-1-ol (-5.755 kcal/mol), azetidin-2-one 3,3-dimethyl-4-(1-aminoethyl) (-5.39 kcal/mol), and lorazepam, 2TMS derivative (-5.246 kcal/mol), exhibited the highest docking scores. These three ligands were assessed by MM-GBSA, which revealed that they bind with the necessary Mpro amino acids in the catalytic groove to cause protein inhibition, including Ser144, Cys145, and His41. The molecular dynamics simulation confirmed the complex rigidity and stability of the docked ligand-Mpro complexes based on the analysis of mean radical variations, root-mean-square fluctuations, solvent-accessible surface area, radius of gyration, and hydrogen bond formation. The study of the postmolecular dynamics confirmation also confirmed that lorazepam, 11-oxa-dispiro[4.0.4.1]undecan-1-ol, and azetidin-2-one-3, 3-dimethyl-4-(1-aminoethyl) interact with similar Mpro binding pockets. The results of our computerized drug design approach may assist in the fight against SARS-CoV-2.

| S-EPMC8389631 | biostudies-literature

Exploration of plant-derived natural polyphenols toward COVID-19 main protease inhibitors: DFT, molecular docking approach, and molecular dynamics simulations.

Project description:Recent outbreaks of coronavirus have brought serious challenges to public health around the world, and it is essential to find effective treatments. In this study, the 3C-like proteinase (3CLpro) of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has been considered as an important drug target because of its role in viral replication. We initially optimized 251 compounds at the PM7 level of theory for docking with 3CLpro, and then we selected the top 12 compounds for further optimization with the B3LYP-D3/6-311G** method and obtained the top four compounds by further molecular docking. Quantum chemistry calculations were performed to predict molecular properties, such as the electrostatic potential and some CDFT descriptors. We also performed molecular dynamics simulations and free energy calculations to determine the relative stability of the selected four potential compounds. We have identified key residues controlling the 3CLpro/ligand binding from per-residue based decomposition of the binding free energy. Convincingly, the comprehensive results support the conclusion that the compounds have the potential to become a candidate for anti-coronavirus treatment.

| S-EPMC8981245 | biostudies-literature

Benchmarking the ability of novel compounds to inhibit SARS-CoV-2 main protease using steered molecular dynamics simulations.

Project description:BackgroundThe SARS-CoV-2 main protease (Mpro) is an attractive target in the COVID-19 drug development process. It catalyzes the polyprotein's translation from viral RNA and specifies a particular cleavage site. Due to the absence of identical cleavage specificity in human cell proteases, targeting Mpro with chemical compounds can obstruct the replication of the virus.MethodsTo explore the potential binding mechanisms of 1,2,3-triazole scaffolds in comparison to co-crystallized inhibitors 11a and 11b towards Mpro, we herein utilized molecular dynamics and enhanced sampling simulation studies.Results and conclusionAll the 1,2,3-triazole scaffolds interacted with catalytic residues (Cys145 and His41) and binding pocket residues of Mpro involving Met165, Glu166, Ser144, Gln189, His163, and Met49. Furthermore, the adequate binding free energy and potential mean force of the topmost compound 3h was comparable to the experimental inhibitors 11a and 11b of Mpro. Overall, the current analysis could be beneficial in developing the SARS-CoV-2 Mpro potential inhibitors.

| S-EPMC9052739 | biostudies-literature

Exploring Toxins for Hunting SARS-CoV-2 Main Protease Inhibitors: Molecular Docking, Molecular Dynamics, Pharmacokinetic Properties, and Reactome Study.

Project description:The main protease (Mpro) is a potential druggable target in SARS-CoV-2 replication. Herein, an in silico study was conducted to mine for Mpro inhibitors from toxin sources. A toxin and toxin-target database (T3DB) was virtually screened for inhibitor activity towards the Mpro enzyme utilizing molecular docking calculations. Promising toxins were subsequently characterized using a combination of molecular dynamics (MD) simulations and molecular mechanics-generalized Born surface area (MM-GBSA) binding energy estimations. According to the MM-GBSA binding energies over 200 ns MD simulations, three toxins-namely philanthotoxin (T3D2489), azaspiracid (T3D2672), and taziprinone (T3D2378)-demonstrated higher binding affinities against SARS-CoV-2 Mpro than the co-crystalized inhibitor XF7 with MM-GBSA binding energies of -58.9, -55.9, -50.1, and -43.7 kcal/mol, respectively. The molecular network analyses showed that philanthotoxin provides a ligand lead using the STRING database, which includes the biochemical top 20 signaling genes CTSB, CTSL, and CTSK. Ultimately, pathway enrichment analysis (PEA) and Reactome mining results revealed that philanthotoxin could prevent severe lung injury in COVID-19 patients through the remodeling of interleukins (IL-4 and IL-13) and the matrix metalloproteinases (MMPs). These findings have identified that philanthotoxin-a venom of the Egyptian solitary wasp-holds promise as a potential Mpro inhibitor and warrants further in vitro/in vivo validation.

| S-EPMC8875976 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data