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Medicinal Chemistry 2020-Jul

In silico study to evaluate the antiviral activity of novel structures against 3C-like protease of Novel Coronavirus (COVID-19) and SARS-CoV

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Kiran Chunduru
Runali Sankhe
Farmiza Begum
Nalini Sodum
Nitesh Kumar
Anoop Kishore
Rekha Shenoy
C Rao
Kavitha Saravu

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Astratto

Background: Globally over 4.3 million laboratory confirmed cases of COVID-19 have been reported from over 105 countries. No FDA approved vaccine or antiviral is available for the treatment of this infection. Zhavoronkov et al., with their generative chemistry pipeline have generated structures that can be potential novel drug-like inhibitors for COVID-19, provided they are validated. 3C-like protease (3CLP) is a homodimeric cysteine protease that is present in coronaviruses. Interestingly, 3CLP is 96.1% structurally similar between SARS-CoV and SARS-CoV-2.

Objective: To evaluate interaction of generated structures with 3CLP of SARS-CoV (RCSB PDB ID: 4MDS).

Method: Crystal structure of human SARS-CoV with a non-covalent inhibitor with resolution: 1.598 Å was obtained and molecular docking was performed to evaluate the interaction with generated structures. The MM-GBSA and IFD-SP were performed to narrow down to the structures with better binding energy and IFD score. The ADME analysis was performed on top 5 hits and further MD simulation was employed for top 2 hits.

Results: In XP docking, IFD-SP and molecular dynamic simulation studies, the top 2 hits 32 and 61 showed interaction with key amino acid residue GLU166. The structure 61, also showed interaction with HIS164. These interactions of generated structure 32 and 61, with GLU166 and HIS164 indicates the binding of selected drug within the close proximity of 3CLP. In the MD simulation the protein-ligand complex of 61 and 4MDS was found to be more stable for 10ns.

Conclusion: These identified structures can be further assessed for their antiviral activity to combat SARS-CoV and COVID-19.

Keywords: 3C- like protease; Anti-viral agent.; Anti-viral agent.Coronavirus; COVID-19; Coronavirus; Molecular Modelling; SARS-CoV.

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