Identifying the natural compound Catechin from tropical mangrove plants as a potential lead candidate against 3CLpro from SARS-CoV-2: An integrated in silico approach.

SARS-CoV-2, a member of beta coronaviruses, is a single-stranded, positive-sense RNA virus responsible for the COVID-19 pandemic. With global fatalities of the pandemic exceeding 4.57 million, it becomes crucial to identify effective therapeutics against the virus. A protease, 3CLpro, is responsible for the proteolysis of viral polypeptides into functional proteins, which is essential for viral pathogenesis. This indispensable activity of 3CLpro makes it an attractive target for inhibition studies. The current study aimed to identify potential lead molecules against 3CLpro of SARS-CoV-2 using a manually curated in-house library of antiviral compounds from mangrove plants. This study employed the structure-based virtual screening technique to evaluate an in-house library of antiviral compounds against 3CLpro of SARS-CoV-2. The library was comprised of thirty-three experimentally proven antiviral molecules extracted from different species of tropical mangrove plants. The molecules in the library were virtually screened using AutoDock Vina, and subsequently, the top five promising 3CLpro-ligand complexes along with 3CLpro-N3 (control molecule) complex were subjected to MD simulations to comprehend their dynamic behaviour and structural stabilities. Finally, the MM/PBSA approach was used to calculate the binding free energies of 3CLpro complexes. Among all the studied compounds, Catechin achieved the most significant binding free energy (-40.3 ± 3.1 kcal/mol), and was closest to the control molecule (-42.8 ± 5.1 kcal/mol), and its complex with 3CLpro exhibited the highest structural stability. Through extensive computational investigations, we propose Catechin as a potential therapeutic agent against SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

Targeting SARS-CoV-2: a systematic drug repurposing approach to identify promising inhibitors against 3C-like proteinase and 2'-O-ribose methyltransferase.

The recent pandemic associated with SARS-CoV-2, a virus of the Coronaviridae family, has resulted in an unprecedented number of infected people. The highly contagious nature of this virus makes it imperative for us to identify promising inhibitors from pre-existing antiviral drugs. Two druggable targets, namely 3C-like proteinase (3CLpro) and 2'-O-ribose methyltransferase (2'-O-MTase) were selected in this study due to their indispensable nature in the viral life cycle. 3CLpro is a cysteine protease responsible for the proteolysis of replicase polyproteins resulting in the formation of various functional proteins, whereas 2'-O-MTase methylates the ribose 2'-O position of the first and second nucleotide of viral mRNA, which sequesters it from the host immune system. The selected drug target proteins were screened against an in-house library of 123 antiviral drugs. Two promising drug molecules were identified for each protein based on their estimated free energy of binding (ΔG), the orientation of drug molecules in the active site and the interacting residues. The selected protein-drug complexes were then subjected to MD simulation, which consists of various structural parameters to equivalently reflect their physiological state. From the virtual screening results, two drug molecules were selected for each drug target protein [Paritaprevir (ΔG = -9.8 kcal/mol) & Raltegravir (ΔG = -7.8 kcal/mol) for 3CLpro and Dolutegravir (ΔG = -9.4 kcal/mol) and Bictegravir (ΔG = -8.4 kcal/mol) for 2'-OMTase]. After the extensive computational analysis, we proposed that Raltegravir, Paritaprevir, Bictegravir and Dolutegravir are excellent lead candidates for these crucial proteins and they could become potential therapeutic drugs against SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

A comprehensive review on promising anti-viral therapeutic candidates identified against main protease from SARS-CoV-2 through various computational methods.

BACKGROUND: The COVID-19 pandemic caused by SARS-CoV-2 has shown an exponential trend of infected people across the planet. Crediting its virulent nature, it becomes imperative to identify potential therapeutic agents against the deadly virus. The 3-chymotrypsin-like protease (3CLpro) is a cysteine protease which causes the proteolysis of the replicase polyproteins to generate functional proteins, which is a crucial step for viral replication and infection. Computational methods have been applied in recent studies to identify promising inhibitors against 3CLpro to inhibit the viral activity. This review provides an overview of promising drug/lead candidates identified so far against 3CLpro through various in silico approaches such as structure-based virtual screening (SBVS), ligand-based virtual screening (LBVS) and drug-repurposing/drug-reprofiling/drug-retasking. Further, the drugs have been classified according to their chemical structures or biological activity into flavonoids, peptides, terpenes, quinolines, nucleoside and nucleotide analogues, protease inhibitors, phenalene and antibiotic derivatives. These are then individually discussed based on the various structural parameters namely estimated free energy of binding (ΔG), key interacting residues, types of intermolecular interactions and structural stability of 3CLpro-ligand complexes obtained from the results of molecular dynamics (MD) simulations. CONCLUSION: The review provides comprehensive information of potential inhibitors identified through several computational methods thus far against 3CLpro from SARS-CoV-2 and provides a better understanding of their interaction patterns and dynamic states of free and ligand-bound 3CLpro structures.

Identification of promising antiviral drug candidates against non-structural protein 15 (NSP15) from SARS-CoV-2: an in silico assisted drug-repurposing study.

The recent COVID-19 pandemic caused by SARS-CoV-2 has recorded a high number of infected people across the globe. The virulent nature of the virus makes it necessary for us to identify promising therapeutic agents in a time-sensitive manner. The current study utilises an in silico based drug repurposing approach to identify potential anti-viral drug candidates targeting non-structural protein 15 (NSP15), i.e. a uridylate specific endoribonuclease of SARS-CoV-2 which plays an indispensable role in RNA processing and viral immune evasion from the host immune system. The NSP15 protein was screened against an in-house library of 123 antiviral drugs obtained from the DrugBank database from which three promising drug candidates were identified based on their estimated binding affinities (ΔG), estimated inhibition constants (Ki), the orientation of drug molecules in the active site and the key interacting residues of NSP15. Molecular dynamics (MD) simulations were performed for the screened drug candidates in complex with NSP15 as well as the apo form of NSP15 to mimic their physiological states. Based on the stable MD simulation trajectories, the binding free energies of the screened NSP15-drug complexes were calculated using the MM/PBSA approach. Two candidate drugs, Simeprevir and Paritaprevir, achieved the lowest binding free energies for NSP15, with a value of -259.522 ± 17.579 and -154.051 ± 33.628 kJ/mol, respectively. In addition, their complexes with NSP15 also exhibited the strongest structural stabilities. Taken together, we propose that Simeprevir and Paritaprevir are promising drug candidates to inhibit NSP15 and may act as potential therapeutic agents against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.