In silico screening and covalent binding of phytochemicals of Ocimum sanctum against SARS-CoV-2 (COVID 19) main protease.

Coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has compelled the scientific community to search for an effective drug that can cure or a vaccine that can prevent the disease. Alternatively, symptomatic treatment and traditional immunity boosters are prescribed. Holy Tulsi (Ocimum sanctum) has been known as an ancient remedy for cure of common cold and respiratory ailment. Several reports have come on virtual screening of phytochemicals including those of Tulsi against various enzymes of the virus. We undertook in silico analysis of the ethanol extracted phytochemicals of Tulsi as inhibitors of SARS-CoV-2 (2019-nCoV) main protease with an approach to look into the possibility of covalent ligand binding with the catalytic residue Cys145, which makes the report unique. The results suggest that the flavonoids and polyphenolic compounds of Tulsi, have potential to covalently bind to the catalytic residue Cys145 of main protease and irreversibly inhibit the viral enzyme. Luteolin-7-O-glucuronide is specially considered for its optimum properties, namely, low toxicity (LD50 5000 mg/kg body weight), high drug-likeness score (0.71), the active site binding free energy (ΔGbind) -19.19 kcal/mol by GBSA method and covalent binding energy -24.23 kcal/mol. Further experimental validations are required to establish the theoretical findings.Communicated by Ramaswamy H. Sarma.

In Silico and In Vitro Evaluations of Fluorophoric Thiazolo-[2,3-b]quinazolinones as Anti-cancer Agents Targeting EGFR-TKD.

Epidermal growth factor receptor tyrosine kinase domain (EGFR-TKD) plays a pivotal role in cellular signaling, growth, and metabolism. The EGFR-TKD is highly expressed in cancer cells and was endorsed as a therapeutic target for cancer management to overcome metastasis, cell proliferation, and angiogenesis. The novel thiazolo-[2,3-b]quinazolinones series were strategically developed by microwave-assisted organic synthesis and multi dominos reactions aimed to identify the potent thiazolo-[2,3-b]quinazolinone inhibitor against EGFR-TKD. This study explores the binding stability and binding strength of newly developed series via molecular docking, molecular dynamics simulation, and MM/PBSA and MM/GBSA calculations. The binding interaction was observed to be through the functional groups on aryl substituents at positions 3 and 5 of the thiazolo-[2, 3-b]quinazolinone scaffold. The methyl substituents at position 8 of the ligands had prominent hydrophobic interactions corroborating their bindings similar to the reference FDA-approved drug erlotinib in the active site. ADMET predictions reveal that derivatives 5ab, 5aq, and 5bq are drug-like and may be effective in in vitro study. Molecular dynamics simulation for 100 ns of docked complexes revealed their stability at the atomistic level. The ΔGbinding of thiazolo-[2,3-b]quinazolinone was found to be 5ab - 22.45, 5aq - 22.23, and 5bq - 20.76 similar to standard drug, and erlotinib - 24.11 kcal/mol was determined by MM/GBSA method. Furthermore, the anti-proliferative activity of leads of thiazolo-[2,3-b]quinazolinones (n = 3) was studied against breast cancer cell line (MCF-7) and non-small lung carcinoma cell line (H-1299). The highest inhibitions in cell proliferation were shown by 5bq derivatives, and the IC50 was found to be 6.5 ± 0.67 µM against MCF-7 and 14.8 µM against H-1299. The noscapine was also taken as a positive control and showed IC50 at higher concentrations 37 ± 1 against MCF-7 and 46.5 ± 1.2 against H-1299.

Monte-Carlo method-based QSAR model to discover phytochemical urease inhibitors using SMILES and GRAPH descriptors.

Urease inhibitors are known to play a vital role in the field of medicine as well as agriculture. Special attention is attributed to the development of novel urease inhibitors with a view to treat the Helicobacter pylori infection. Amongst a number of urease inhibitors, a large number of molecules fail in vivo and in clinical trials due to their hydrolytic instability and toxicity profile. The search for potential inhibitors may require screening of large and diverse databases of small molecules and to design novel molecules. We developed a Monte-Carlo method-based QSAR model to predict urease inhibiting potency of molecules using SMILES and GRAPH descriptors on an existing diverse database of urease inhibitors. The QSAR model satisfies all the statistical parameters required for acceptance as a good model. The model is applied to identify urease inhibitors among the wide range of compounds in the phytochemical database, NPACT, as a test case. We combine the ligand-based and structure-based drug discovery methods to improve the accuracy of the prediction. The method predicts pIC50 and estimates docking score of compounds in the database. The method may be applied to any other database or compounds designed in silico to discover novel drugs targeting urease.Communicated by Ramaswamy H. Sarma.