Concentration-Dependent Multi-Potentiality of L-Arginine: Antimicrobial Effect, Hydroxyapatite Stability, and MMPs Inhibition

This study’s objective was to examine L-arginine (L-arg) supplementation’s effect on mono-species biofilm (Streptococcus mutans/Streptococcus sanguinis) growth and underlying enamel substrates. The experimental groups were 1%, 2%, and 4% arg, and 0.9% NaCl was used as the vehicle control. Sterilised enamel blocks were subjected to 7-day treatment with test solutions and S. mutans/S. sanguinis inoculum in BHI. Post-treatment, the treated biofilms stained for live/dead bacterial cells were analysed using confocal microscopy. The enamel specimens were analysed using X-ray diffraction crystallography (XRD), Raman spectroscopy (RS), and transmission electron microscopy (TEM). The molecular interactions between arg and MMP-2/MMP-9 were determined by computational molecular docking and MMP assays. With increasing arg concentrations, bacterial survival significantly decreased (p < 0.05). The XRD peak intensity with 1%/2% arg was significantly higher than with 4% arg and the control (p < 0.05). The bands associated with the mineral phase by RS were significantly accentuated in the 1%/2% arg specimens compared to in other groups (p < 0.05). The TEM analysis revealed that 4% arg exhibited an ill-defined shape of enamel crystals. Docking of arg molecules to MMPs appears feasible, with arg inhibiting MMP-2/MMP-9 (p < 0.05). L-arginine supplementation has an antimicrobial effect on mono-species biofilm. L-arginine treatment at lower (1%/2%) concentrations exhibits enamel hydroxyapatite stability, while the molecule has the potential to inhibit MMP-2/MMP-9.

Computational Molecular Docking Analysis and Visualisation of Anthocyanins for Anticancer Activity

Various invitro and computational methods were implemented to evaluate the anticancer potential of anthocyanidins, namely cyanidin, malvidin, delphinidin, peonidin, pelargonidin, and petunidin. These anthocyanidins were docked with CDK-2, CDK-6 and IGF-1R kinase proteins. Additionally, known inhibitors (KIs) such as SU9516, Palbociclib, OSI-906 are compared with their respective macromolecules, including, CDK-2, CDK-6 and IGF-1R kinase, in to compare results of the study based on Lipinski rule of 5. The Auto Dock Tool (Autodock 4) was used for molecular docking, and the docked complex compounds were visualised and interpreted using the Bio via Discovery Studio 2020 client. The Docking results obtained showed a very good inhibitory binding to almost all the selected cancer proteins, and these compounds might be a potential drug molecule.

Computational molecular docking and virtual screening revealed promising SARS-CoV-2 drugs

The pandemic of novel coronavirus disease 2019 (COVID-19) has rampaged the world with more than 58.4 million confirmed cases and over 1.38 million deaths across the world by November 23, 2020. There is an urgent need to identify effective drugs and vaccines to fight against the virus. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to the family of coronaviruses consisting of four structural and 16 non-structured proteins. Three non-structural proteins such as main protease (Mpro), papain-like protease (PLpro), and RNA-dependent RNA polymerase (RdRp) are believed to play a crucial role in the virus replication. We applied a computational ligand-receptor binding modeling and performed a comprehensive virtual screening on the FDA-approved drugs against these three SARS-CoV-2 proteins using AutoDock Vina, Glide, and rDock. Our computational studies identified six novel ligands as potential inhibitors against SARS-CoV-2, including antiemetics Rolapitant and Ondansetron for Mpro; Labetalol and Levomefolic acid for PLpro; and Leucal and antifungal Natamycin for RdRp. Molecular dynamics simulation confirmed the stability of the ligand-protein complexes. The result of our analysis with some other suggested drugs indicated that chloroquine and hydroxychloroquine had high binding energy (low inhibitory effect) with all three proteins—Mpro, PLpro, and RdRp. In summary, our computational molecular docking approach and virtual screening identified some promising candidate SARS-CoV-2 inhibitors that may be considered for further clinical studies.