Structure-based virtual screening against SARS-3CL(pro) to identify novel non-peptidic hits.
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ABSTRACT:
SUBMITTER: Mukherjee P
PROVIDER: S-EPMC7127700 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The pandemic, COVID-19, has spread worldwide and affected millions of people. There is an urgent need, therefore, to find a proper treatment for the novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the causative agent. This paper focuses on identifying inhibitors that target SARS-CoV-2 proteases, PLPRO and 3CLPRO, which control the duplication and manages the life cycle of SARS-CoV-2. We have carried out detailed in silico Virtual high-throughput screening using Food and Drug Administration (FDA) approved drugs from the Zinc database, COVID-19 clinical trial compounds from Pubchem database, Natural compounds from Natural Product Activity and Species Source (NPASS) database and Maybridge database against PLPRO and 3CLPRO proteases. After thoroughly analyzing the screening results, we found five compounds, Bemcentinib, Pacritinib, Ergotamine, MFCD00832476, and MFCD02180753 inhibit PLPRO and six compounds, Bemcentinib, Clofazimine, Abivertinib, Dasabuvir, MFCD00832476, Leuconicine F inhibit the 3CLPRO. These compounds are stable within the protease proteins' active sites at 20ns MD simulation. The stability is revealed by hydrogen bond formations, hydrophobic interactions, and salt bridge interactions. Our study results also reveal that the selected five compounds against PLPRO and the six compounds against 3CLPRO bind to their active sites with good binding free energy. These compounds that inhibit the activity of PLPRO and 3CLPRO may, therefore, be used for treating COVID-19 infection.
| S-EPMC8018918 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic, caused by the SARS-CoV-2 virus, has ravaged lives across the globe since December 2019, and new cases are still on the rise. Peoples' ongoing sufferings trigger scientists to develop safe and effective remedies to treat this deadly viral disease. While repurposing the existing FDA-approved drugs remains in the front line, exploring drug candidates from synthetic and natural compounds is also a viable alternative. This study employed a comprehensive computational approach to screen inhibitors for SARS-CoV-2 3CL-PRO (also known as the main protease), a prime molecular target to treat coronavirus diseases.MethodsWe performed 100 ns GROMACS molecular dynamics simulations of three high-resolution X-ray crystallographic structures of 3CL-PRO. We extracted frames at 10 ns intervals to mimic conformational diversities of the target protein in biological environments. We then used AutoDock Vina molecular docking to virtual screen the Sigma-Aldrich MyriaScreen Diversity Library II, a rich collection of 10,000 druglike small molecules with diverse chemotypes. Subsequently, we adopted in silico computation of physicochemical properties, pharmacokinetic parameters, and toxicity profiles. Finally, we analyzed hydrogen bonding and other protein-ligand interactions for the short-listed compounds.ResultsOver the 100 ns molecular dynamics simulations of 3CL-PRO's crystal structures, 6LZE, 6M0K, and 6YB7, showed overall integrity with mean Cα root-mean-square deviation (RMSD) of 1.96 (±0.35) Å, 1.98 (±0.21) Å, and 1.94 (±0.25) Å, respectively. Average root-mean-square fluctuation (RMSF) values were 1.21 ± 0.79 (6LZE), 1.12 ± 0.72 (6M0K), and 1.11 ± 0.60 (6YB7). After two phases of AutoDock Vina virtual screening of the MyriaScreen Diversity Library II, we prepared a list of the top 20 ligands. We selected four promising leads considering predicted oral bioavailability, druglikeness, and toxicity profiles. These compounds also demonstrated favorable protein-ligand interactions. We then employed 50-ns molecular dynamics simulations for the four selected molecules and the reference ligand 11a in the crystallographic structure 6LZE. Analysis of RMSF, RMSD, and hydrogen bonding along the simulation trajectories indicated that S51765 would form a more stable protein-ligand complexe with 3CL-PRO compared to other molecules. Insights into short-range Coulombic and Lennard-Jones potentials also revealed favorable binding of S51765 with 3CL-PRO.ConclusionWe identified a potential lead for antiviral drug discovery against the SARS-CoV-2 main protease. Our results will aid global efforts to find safe and effective remedies for COVID-19.
| S-EPMC8051358 | biostudies-literature
Project description:COVID-19 caused by a novel coronavirus (SARS-CoV-2) has been spreading all over the world since the end of 2019, and no specific drug has been developed yet. 3C-like protease (3CLpro) acts as an important part of the replication of novel coronavirus and is a promising target for the development of anticoronavirus drugs. In this paper, eight machine learning models were constructed using naïve Bayesian (NB) and recursive partitioning (RP) algorithms for 3CLpro on the basis of optimized two-dimensional (2D) molecular descriptors (MDs) combined with ECFP_4, ECFP_6, and MACCS molecular fingerprints. The optimal models were selected according to the results of 5-fold cross verification, test set verification, and external test set verification. A total of 5766 natural compounds from the internal natural product database were predicted, among which 369 chemical components were predicted to be active compounds by the optimal models and the EstPGood values were more than 0.6, as predicted by the NB (MD + ECFP_6) model. Through ADMET analysis, 31 compounds were selected for further biological activity determination by the fluorescence resonance energy transfer (FRET) method and cytopathic effect (CPE) detection. The results indicated that (+)-shikonin, shikonin, scutellarein, and 5,3',4'-trihydroxyflavone showed certain activity in inhibiting SARS-CoV-2 3CLpro with the half-maximal inhibitory concentration (IC50) values ranging from 4.38 to 87.76 μM. In the CPE assay, 5,3',4'-trihydroxyflavone showed a certain antiviral effect with an IC50 value of 8.22 μM. The binding mechanism of 5,3',4'-trihydroxyflavone with SARS-CoV-2 3CLpro was further revealed through CDOCKER analysis. In this study, 3CLpro prediction models were constructed based on machine learning algorithms for the prediction of active compounds, and the activity of potential inhibitors was determined by the FRET method and CPE assay, which provide important information for further discovery and development of antinovel coronavirus drugs.
| S-EPMC8631171 | biostudies-literature
Project description:We prepared the three-dimensional model of the SARS-CoV-2 (aka 2019-nCoV) 3C-like protease (3CL pro) using the crystal structure of the highly similar (96% identity) ortholog from the SARS-CoV. All residues involved in the catalysis, substrate binding and dimerisation are 100% conserved. Comparison of the polyprotein PP1AB sequences showed 86% identity. The 3C-like cleavage sites on the coronaviral polyproteins are highly conserved. Based on the near-identical substrate specificities and high sequence identities, we are of the opinion that some of the previous progress of specific inhibitors development for the SARS-CoV enzyme can be conferred on its SARS-CoV-2 counterpart. With the 3CL pro molecular model, we performed virtual screening for purchasable drugs and proposed 16 candidates for consideration. Among these, the antivirals ledipasvir or velpatasvir are particularly attractive as therapeutics to combat the new coronavirus with minimal side effects, commonly fatigue and headache. The drugs Epclusa (velpatasvir/sofosbuvir) and Harvoni (ledipasvir/sofosbuvir) could be very effective owing to their dual inhibitory actions on two viral enzymes.
| S-EPMC7062204 | biostudies-literature
Project description:3CLpro is the main protease of the novel coronavirus (SARS-CoV-2) responsible for their intracellular duplication. Based on virtual screening technology and molecular dynamics simulation, we found 23 approved clinical drugs such as Viomycin, Capastat, Carfilzomib and Saquinavir, which showed high affinity with the 3CLpro active sites. These findings showed that there were potential drugs that inhibit SARS-Cov-2's 3CLpro in the current clinical drug library, and these drugs can be further tested or chemically modified for the treatment of COVID-19.Communicated by Ramaswamy H. Sarma.
| S-EPMC7544985 | biostudies-literature
Project description:It has been confirmed that the new coronavirus SARS-CoV-2 caused the global pandemic of coronavirus disease 2019 (COVID-19). Studies have found that 3-chymotrypsin-like protease (3CLpro) is an essential enzyme for virus replication, and could be used as a potential target to inhibit SARS-CoV-2. In this work, 3CLpro was used as the target to complete the high-throughput virtual screening of the FDA-approved drugs, and Indinavir and other 10 drugs with high docking scores for 3CLpro were obtained. Studies on the binding pattern of 3CLpro and Indinavir found that Indinavir could form the stable hydrogen bond (H-bond) interactions with the catalytic dyad residues His41-Cys145. Binding free energy study found that Indinavir had high binding affinity with 3CLpro. Subsequently, molecular dynamics simulations were performed on the 3CLpro and 3CLpro-Indinavir systems, respectively. The post-dynamic analyses showed that the conformational state of the 3CLpro-Indinavir system transformed significantly and the system tended to be more stable. Moreover, analyses of the residue interaction network (RIN) and H-bond occupancy revealed that the residue-residue interaction at the catalytic site of 3CLpro was significantly enhanced after binding with Indinavir, which in turn inactivated the protein. In short, through this research, we hope to provide more valuable clues against COVID-19.
| S-EPMC7993695 | biostudies-literature
Project description:3CL proteases (3CLpro) are only found in RNA viruses and have a central role in polyprotein processing during replication. Therefore, 3CLpro has emerged as promising drug target for therapeutic treatment of infections caused by Coronaviruses. In the light of the recent major outbreak of the SARS-CoV-2 virus and the continuously rising numbers of infections and casualties, there is an urgent need for quickly available drugs or vaccines to stop the current COVID-19 pandemic. Repurposing of approved drugs as 3CLpro inhibitors could dramatically shorten the period up to approval as therapeutic against SARS-CoV-2, since pharmacokinetics and toxicity is already known. Several known drugs, e.g. oxytetracycline, doxorubicin, kanamycin, cefpiramide, teniposide, proanthocyanidin and salvianolic acid B, but also not-approved active compounds from the ZINC15 library were identified as new potential inhibitors of 3CLpro by using different complementary virtual screening and docking approaches. These compounds have the potential to be further optimized using structure based drug design as demonstrated for oxytetracycline.
| S-EPMC7832737 | biostudies-literature
Project description:BackgroundThe emergence of Coronavirus disease (COVID-19) has been declared a pandemic and made a medical emergency worldwide. Various attempts have been made, including optimizing effective treatments against the disease or developing a vaccine. Since the SARS-CoV-2 protease crystal structure has been discovered, searching for its inhibitors by in silico technique becomes possible.ObjectiveThis study aims to virtually screen the potential of phytoconstituents from the Begonia genus as 3Cl pro-SARS-CoV- 2 inhibitors, based on its crucial role in viral replication, hence making these proteases "promising" for the anti-SARS-CoV-2 target.MethodsIn silico screening was carried out by molecular docking on the web-based program DockThor and validated by a retrospective method. Predictive binding affinity (Dock Score) was used for scoring the compounds. Further molecular dynamics on Desmond was performed to assess the complex stability.ResultsVirtual screening protocol was valid with the area under curve value 0.913. Molecular docking revealed only β-sitosterol- 3-O-β-D-glucopyranoside with a lower docking score of - 9.712 kcal/mol than positive control of indinavir. The molecular dynamic study showed that the compound was stable for the first 30 ns simulations time with Root Mean Square Deviation <3 Å, despite minor fluctuations observed at the end of simulation times. Root Mean Square Fluctuation of catalytic sites HIS41 and CYS145 was 0.756 Å and 0.773 Å, respectively.ConclusionsThis result suggests that β-sitosterol-3-O-β-Dglucopyranoside might be a prospective metabolite compound that can be developed as anti-SARS-CoV-2.
| S-EPMC10365649 | biostudies-literature
Project description:3-Chymotrypsin-like protease (3CLpro) is a virally encoded main proteinase that is pivotal for the viral replication across a broad spectrum of coronaviruses. This study aims to discover the naturally occurring SARS-CoV-2 3CLpro inhibitors from herbal constituents, as well as to investigate the inhibitory mechanism of the newly identified efficacious SARS-CoV-2 3CLpro inhibitors. Following screening of the inhibitory potentials of eighty herbal products against SARS-CoV-2 3CLpro, Ginkgo biloba leaves extract (GBLE) was found with the most potent SARS-CoV-2 3CLpro inhibition activity (IC50 = 6.68 μg/mL). Inhibition assays demonstrated that the ginkgolic acids (GAs) and the bioflavones isolated from GBLE displayed relatively strong SARS-CoV-2 3CLpro inhibition activities (IC50 < 10 μM). Among all tested constituents, GA C15:0, GA C17:1 and sciadopitysin displayed potent 3CLpro inhibition activities, with IC50 values of less than 2 μM. Further inhibition kinetic studies and docking simulations clearly demonstrated that two GAs and sciadopitysin strongly inhibit SARS-CoV-2 3CLprovia a reversible and mixed inhibition manner. Collectively, this study found that both GBLE and the major constituents in this herbal product exhibit strong SARS-CoV-2 3CLpro inhibition activities, which offer several promising leading compounds for developing novel anti-COVID-19 medications via targeting on 3CLpro.
| S-EPMC8061081 | biostudies-literature
Project description:The outbreak of 2019 novel coronavirus (COVID-19) has caused serious threat to public health. Discovery of new anti-COVID-19 drugs is urgently needed. Fortunately, the crystal structure of COVID-19 3CL proteinase was recently resolved. The proteinase has been identified as a promising target for drug discovery in this crisis. Here, a dataset including 2030 natural compounds was screened and refined based on the machine learning and molecular docking. The performance of six machine learning (ML) methods of predicting active coronavirus inhibitors had achieved satisfactory accuracy, especially, the AUC (Area Under ROC Curve) scores with fivefold cross-validation of Logistic Regression (LR) reached up to 0.976. Comprehensive ML prediction and molecular docking results accounted for the compound Rutin, which was approved by NMPA (National Medical Products Administration), exhibited the best AUC and the most promising binding affinity compared to other compounds. Therefore, Rutin might be a promising agent in anti-COVID-19 drugs development.
| S-EPMC7561382 | biostudies-literature