Towards the discovery of potential RdRp inhibitors for the treatment of COVID-19: structure guided virtual screening, computational ADME and molecular dynamics study.

Coronavirus disease 2019 (COVID-19) has become a major challenge affecting almost every corner of the world, with more than five million deaths worldwide. Despite several efforts, no drug or vaccine has shown the potential to check the ever-mutating SARS-COV-2. The emergence of novel variants is a major concern increasing the need for the discovery of novel therapeutics for the management of this pandemic. Out of several potential drug targets such as S protein, human ACE2, TMPRSS2 (transmembrane protease serine 2), 3CLpro, RdRp, and PLpro (papain-like protease), RNA-dependent RNA polymerase (RdRP) is a vital enzyme for viral RNA replication in the mammalian host cell and is one of the legitimate targets for the development of therapeutics against this disease. In this study, we have performed structure-based virtual screening to identify potential hit compounds against RdRp using molecular docking of a commercially available small molecule library of structurally diverse and drug-like molecules. Since non-optimal ADME properties create hurdles in the clinical development of drugs, we performed detailed in silico ADMET prediction to facilitate the selection of compounds for further studies. The results from the ADMET study indicated that most of the hit compounds had optimal properties. Moreover, to explore the conformational dynamics of protein-ligand interaction, we have performed an atomistic molecular dynamics simulation which indicated a stable interaction throughout the simulation period. We believe that the current findings may assist in the discovery of drug candidates against SARS-CoV-2.

Green synthesis of silver nanoformulation of Scindapsus officinalis as potent anticancer and predicted anticovid alternative: Exploration via experimental and computational methods

The current study was focused on the investigation of anticancer activity of Scindapsus Officinalis fruit extract embedded silver nanoparticles (So-AgNPs) followed by anticovid activity prognosis of major phytocompounds, which participate in nanoformulation synthesis. The synthesis process involved the addition of AgNO3 solution (1 mM) and color change of the extract from light brown to dark, confirmed the formation of silver nanoparticles. Further, the characterization of synthesized So-AgNPs were done using different spectroscopical and microscopical techniques. FTIR spectra of So-AgNPs indicated vibrational peaks of polyphenolic hydroxyl groups, which are responsible for the stabilization of nanoformulation. Others microscopy methods such as SEM, TEM, XRD, and EDX illustrated that the synthesized So-AgNPs consist irregular size, spherical shape and thoroughly dispersed above the plane. Anticancer evaluation illustrated that the So-AgNPs have dose dependent anti-breast and anti-hepatic cancer activity (range of 97.72 ± 0.42 – 54.86 ± 0.46 % cell viability), which were noticed more effective than raw fruit extract of Scindapsus Officinalis. The computational anticovid prediction of major phyto-compound of the extract [which designate as inhibitor 1: ((2R,3S,4S,5R)-2-(hydroxymethyl)-6-(((1S,5S)-1-methyl-5-(2-methylprop-1-en-1-yl)cyclopent-2-en-1-yl)oxy)tetrahydro-2H-pyran-3,4,5-triol)] illustrated moderate tendency to interact with corona main protease enzyme (expected pIC > 6 μM). However, the molecular docking and dynamics studies showed that selected compounds have moderate tendency to interact human dihydrofolate reductase and topoisomerase 1 enzyme. The accomplished approach shows that So-AgNPs with adsorbed phytocompounds on its surface consist valuable experimentally proved anticancer potency and computationally predicted anticovid effect. Thus, the formulation can be used as an alternative to the covid infected cancer population.

An in-silico analysis of ivermectin interaction with potential SARS-CoV-2 targets and host nuclear importin α.

Ivermectin (IVM) is a broad-spectrum antiparasitic agent, having inhibitory potential against wide range of viral infections. It has also been found to hamper SARS-CoV-2 replication in vitro, and its precise mechanism of action against SARS-CoV-2 is yet to be understood. IVM is known to interact with host importin (IMP)α directly and averts interaction with IMPß1, leading to the prevention of nuclear localization signal (NLS) recognition. Therefore, the current study seeks to employ molecular docking, molecular mechanics generalized Born surface area (MM-GBSA) analysis and molecular dynamics simulation studies for decrypting the binding mode, key interacting residues as well as mechanistic insights on IVM interaction with 15 potential drug targets associated with COVID-19 as well as IMPα. Among all COVID-19 targets, the non-structural protein 9 (Nsp9) exhibited the strongest affinity to IVM showing -5.30 kcal/mol and -84.85 kcal/mol binding energies estimated by AutoDock Vina and MM-GBSA, respectively. However, moderate affinity was accounted for IMPα amounting -6.9 kcal/mol and -66.04 kcal/mol. Stability of the protein-ligand complexes of Nsp9-IVM and IMPα-IVM was ascertained by 100 ns trajectory of all-atom molecular dynamics simulation. Structural conformation of protein in complex with docked IVM exhibited stable root mean square deviation while root mean square fluctuations were also found to be consistent. In silico exploration of the potential targets and their interaction profile with IVM can assist experimental studies as well as designing of COVID-19 drugs. Communicated by Ramaswamy H. Sarma.