Dataset Information

Targeting SARS-CoV-2 main protease: structure based virtual screening, in silico ADMET studies and molecular dynamics simulation for identification of potential inhibitors.

ABSTRACT: COVID-19 pandemic has created a healthcare crisis across the world and has put human life under life-threatening circumstances. The recent discovery of the crystallized structure of the main protease (M_pro) from SARS-CoV-2 has provided an opportunity for utilizing computational tools as an effective method for drug discovery. Targeting viral replication has remained an effective strategy for drug development. M_pro of SARS-COV-2 is the key protein in viral replication as it is involved in the processing of polyproteins to various structural and nonstructural proteins. Thus, M_pro represents a key target for the inhibition of viral replication specifically for SARS-CoV-2. We have used a virtual screening strategy by targeting M_pro against a library of commercially available compounds to identify potential inhibitors. After initial identification of hits by molecular docking-based virtual screening further MM/GBSA, predictive ADME analysis, and molecular dynamics simulation were performed. The virtual screening resulted in the identification of twenty-five top scoring structurally diverse hits that have free energy of binding (ΔG) values in the range of -26-06 (for compound AO-854/10413043) to -59.81 Kcal/mol (for compound 329/06315047). Moreover, the top-scoring hits have favorable AMDE properties as calculated using in silico algorithms. Additionally, the molecular dynamics simulation revealed the stable nature of protein-ligand interaction and provided information about the amino acid residues involved in binding. Overall, this study led to the identification of potential SARS-CoV-2 M_pro hit compounds with favorable pharmacokinetic properties. We believe that the outcome of this study can help to develop novel M_pro inhibitors to tackle this pandemic. Communicated by Ramaswamy H. Sarma.

SUBMITTER: Uniyal A

PROVIDER: S-EPMC7754935 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Coronavirus (covid-19) infection is considered to be deadliest ever pandemic experienced by the human being. It has very badly affected the socio-economic health of human and stuck the scientific community to think and rethink about its complete eradication. But due to no effective treatment or unavailability of vaccine the health professional could not show any significant improvement to control the pandemic. The situation needs newer molecule, vaccine or effective treatment to control covid-19 infection. Different target in viruses has been explored and proteases enzymes were found to be therapeutically effective target for the design of potential anti-covid-19 molecule as it plays the vital role in viral replication and assembly. Structure-based drug design was employed to discover the small molecule of anti-covid-19. Here we considered the small library of naturally occurring polyphenolic compounds and molecular docking, Molecular dynamics (MD) simulations, free binding energy calculation and in-silico ADME calculations to identify the newer HITs. Based upon their score the two molecules were identified as promising candidate. The docking scores were found to be -7.643 and -7.065 for the HIT1 and HIT-2 respectively. In MD simulations study the RMSD values were found to be 4.3 Å & 4.9 Å respectively. To validate these results MM-GBSA was performed and their binding free energies were computationally determined. The prime energy values of identified HITs (-13412.45 & -13441.8 kJ/mole) were found to be very close proximity to reference molecule (-13493.05 kJ/mole). Then in-silico ADME calculations were performed to calculate the drug likeliness identified HITs. BY considering all the values comparative to reference molecule and obtained in-silico pharmacokinetic properties of identified HITs we can suggest that HIT-1 and HIT-2 would be the most promising molecules that can inhibit the main protease enzyme of covid-19. These two molecules would become the potential drug candidate for the treatment of covid-19 infections.

Project description:Background: Due to the constant mutation of virus and the lack of specific therapeutic drugs, the coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still poses a huge threat to the health of people, especially those with underlying diseases. Therefore, drug discovery against the SARS-CoV-2 remains of great significance. Methods: With the main protease of virus as the inhibitor target, 9,614 genistein derivatives were virtually screened by LeDock and AutoDock Vina, and the top 20 compounds with highest normalized scores were obtained. Molecular dynamics simulations were carried out for studying interactions between these 20 compounds and the target protein. The drug-like properties, activity, and ADMET of these compounds were also evaluated by DruLiTo software or online server. Results: Twenty compounds, including compound 11, were screened by normalized molecular docking, which could bind to the target through multiple non-bonding interactions. Molecular dynamics simulation results showed that compounds 2, 4, 5, 11, 13, 14, 17, and 18 had the best binding force with the target protein of SARS-CoV-2, and the absolute values of binding free energies all exceeded 50 kJ/mol. The drug-likeness properties indicated that a variety of compounds including compound 11 were worthy of further study. The results of bioactivity score prediction found that compounds 11 and 12 had high inhibitory activities against protease, which indicated that these two compounds had the potential to be further developed as COVID-19 inhibitors. Finally, compound 11 showed excellent predictive ADMET properties including high absorption and low toxicity. Conclusion: These in silico work results show that the preferred compound 11 (ZINC000111282222), which exhibited strong binding to SARS-CoV-2 main protease, acceptable drug-like properties, protease inhibitory activity and ADMET properties, has great promise for further research as a potential therapeutic agent against COVID-19.

Project description:The continued toll of COVID-19 has halted the smooth functioning of civilization on a global scale. With a limited understanding of all the essential components of viral machinery and the lack of structural information of this new virus, initial drug discovery efforts had limited success. The availability of high-resolution crystal structures of functionally essential SARS-CoV-2 proteins, including 3CLpro, supports the development of target-specific therapeutics. 3CLpro, the main protease responsible for the processing of viral polypeptide, plays a vital role in SARS-CoV-2 viral replication and translation and is an important target in other coronaviruses. Additionally, 3CLpro is the target of repurposed drugs, such as lopinavir and ritonavir. In this study, target proteins were retrieved from the protein data bank (PDB IDs: 6 M03, 6LU7, 2GZ7, 6 W63, 6SQS, 6YB7, and 6YVF) representing different open states of the main protease to accommodate macromolecular substrate. A hydroxyethylamine (HEA) library was constructed from harvested chemical structures from all the series being used in our laboratories for screening against malaria and Leishmania parasites. The database consisted of ∼1000 structure entries, of which 70% were new to ChemSpider at the time of screening. This in-house library was subjected to high throughput virtual screening (HTVS), followed by standard precision (SP) and then extra precision (XP) docking (Schrodinger LLC 2021). The ligand strain and complex energy of top hits were calculated by Molecular Mechanics Generalized Born Surface Area (MM/GBSA) method. Promising hit compounds (n = 40) specifically binding to 3CLpro with high energy and average MM/GBSA scores were then subjected to (100-ns) MD simulations. Using this sequential selection followed by an in-silico validation approach, we found a promising HEA-based compound (N,N'-((3S,3'S)-piperazine-1,4-diylbis(3-hydroxy-1-phenylbutane-4,2-diyl))bis(2-(5-methyl-1,3-dioxoisoindolin-2-yl)-3-phenylpropanamide)), which showed high in vitro antiviral activity against SARS-CoV-2. Further to reduce the size of the otherwise larger ligand, a pharmacophore-based predicted library of ∼42 derivatives was constructed, which were added to the previous compound library and rescreened virtually. Out of several hits from the predicted library, two compounds were synthesized, tested against SARS-CoV-2 culture, and found to have markedly improved antiviral activity.

Dataset Information

Targeting SARS-CoV-2 main protease: structure based virtual screening, in silico ADMET studies and molecular dynamics simulation for identification of potential inhibitors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets