In silico study of potential SARS-CoV-2 antagonist from Clitoria ternatea.

Objectives: In this study, we implemented a structure-based virtual screening protocol in search of natural bioactive compounds in Clitoria ternatea that could inhibit the viral Mpro. Methods: A library of twelve main bioactive compounds in C. ternatea was created from PubChem database by minimizing ligand structure in PyRx software to increase the ligand flexibility. Molecular docking studies were performed by targeting Mpro (PDB ID: 6lu7) via Discovery Studio Visualiser and PyRx platforms. Top hits compounds were then selected to study their Adsorption, distribution, metabolism, excretion, and toxicity (ADMET) and drug likeness properties through pkCSM pharmacokinetics tool to understand the stability, interaction, conformational changes, and pharmaceutical relevant parameters. Results: This investigation found that, in the molecular docking simulation, four bioactive compounds (procyanidin A2 [-9.3 kcal/mol], quercetin-3-rutinoside [-8.9 kcal/mol], delphinidin-3-O-glucoside [-8.3 kcal/mol], and ellagic acid [-7.4 kcal/mol]) showed producing the strongest binding affinity to the Mpro of severe acute respiratory syndrome coronavirus 2, as compared to positive control (N3 inhibitor) (-7.5 kcal/mol). These binding energies were found to be favorable for an efficient docking and resultant. In addition, the stability of quercetin-3-rutinoside and ellagic acid is higher without any unfavorable bond. The ADMET and drug likeness of these two compounds were found that they are considered an effective and safe coronavirus disease 2019 (COVID-19) inhibitors through Lipinski's Rule, absorption, distribution, metabolism, and toxicity properties. Conclusion: From these results, it was concluded that C. ternatea possess potential therapeutic properties against COVID-19.

Computational Screening of Inhibitory Compounds for SARS-Cov-2 3CL Protease with a Database Consisting of Approved and Investigational Chemicals.

Computational screening is one of the fundamental techniques in drug discovery. Each compound in a chemical database is bound to the target protein in virtual, and candidate compounds are selected from the binding scores. In this work, we carried out combinational computation of docking simulation to generate binding poses and molecular mechanics calculation to estimate binding scores. The coronavirus infectious disease has spread worldwide, and effective chemotherapy is strongly required. The viral 3-chymotrypsin-like (3CL) protease is a good target of low molecular-weight inhibitors. Hence, computational screening was performed to search for inhibitory compounds acting on the 3CL protease. As a preliminary assessment of the performance of this approach, we used 51 compounds for which inhibitory activity had already been confirmed. Docking simulations and molecular mechanics calculations were performed to evaluate binding scores. The preliminary evaluation suggested that our approach successfully selected the inhibitory compounds identified by the experiments. The same approach was applied to 8820 compounds in a database consisting of approved and investigational chemicals. Hence, docking simulations, molecular mechanics calculations, and re-evaluation of binding scores including solvation effects were performed, and the top 200 poses were selected as candidates for experimental assays. Consequently, 25 compounds were chosen for in vitro measurement of the enzymatic inhibitory activity. From the enzymatic assay, 5 compounds were identified to have inhibitory activities against the 3CL protease. The present work demonstrated the feasibility of a combination of docking simulation and molecular mechanics calculation for practical use in computational virtual screening.

Computational screening for investigating the synergistic regulatory potential of drugs and phytochemicals in combination with 2-deoxy-D-glucose against SARS-CoV-2.

COVID-19 disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was declared a global pandemic by the World Health Organization (WHO) in March 2020. Since then, the SARS-CoV-2 virus has impacted millions of lives worldwide. Various preclinical and clinical trials on the treatment of COVID-19 disease have revealed that the drugs that work in combination are more likely to reduce reinfection and multi-organ failure. Considering the combination drug therapy, herein, we performed a systematic computational study starting with the formation of sixty-two combinations of drugs and phytochemicals with 2-deoxy-D-glucose (2-DG). The top nineteen combinations resulting from Drug-Drug Interaction (DDI) analysis were selected for individual and multiple-ligand-simultaneous docking (MLSD) study with a host target Serine Protease (TMPRSS2; PDB ID: 7MEQ) and two viral targets, Main Protease (3CLpro; PDB ID: 6LU7) and Uridylate-Specific Endoribonuclease (NSP15; PDB ID: 6VWW). We found that the resulting drugs and phytochemicals in combination with 2-DG shows better binding than the individual compounds. We performed the re-docking of the top three drug combinations by utilizing the polypharmacology approach to validate the binding patterns of drug combinations with multiple targets for verifying the best drug combinatorial output obtained by blind docking. A strong binding affinity pattern was observed for 2-DG + Ruxolitinib (NIH-recommended drug), 2-DG + Telmisartan (phase 4 clinical trial drug), and 2-DG + Punicalagin (phytochemical) for all the selected targets. Additionally, we conducted multiple-ligand-simultaneous molecular dynamics (MLS-MD) simulations on the selected targets with the 2-DG + Ruxolitinib combination. The MLS-MD analysis of the drug combinations shows that stabilization of the interaction complexes could have significant inhibition potential against SARS CoV-2. This study provides an insight into developing drug combinations utilizing integrated computational approaches to uncover their potential in synergistic drug therapy. Supplementary Information: The online version contains supplementary material available at 10.1007/s11224-022-02049-0.

Potential Drugs Targeting Early Innate Immune Evasion of SARS-Coronavirus 2 via 2'-O-Methylation of Viral RNA.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causing the COVID-19 respiratory disease pandemic utilizes unique 2'-O-methyltransferase (2'-O-MTase) capping machinery to camouflage its RNA from innate immune recognition. The nsp16 catalytic subunit of the 2'-O-MTase is unusual in its requirement for a stimulatory subunit (nsp10) to catalyze the ribose 2'-O-methylation of the viral RNA cap. Here we provide a computational basis for drug repositioning or de novo drug development based on three differential traits of the intermolecular interactions of the SARS-CoV-2-specific nsp16/nsp10 heterodimer, namely: (1) the S-adenosyl-l-methionine-binding pocket of nsp16, (2) the unique "activating surface" between nsp16 and nsp10, and (3) the RNA-binding groove of nsp16. We employed ≈9000 U.S. Food and Drug Administration (FDA)-approved investigational and experimental drugs from the DrugBank repository for docking virtual screening. After molecular dynamics calculations of the stability of the binding modes of high-scoring nsp16/nsp10-drug complexes, we considered their pharmacological overlapping with functional modules of the virus-host interactome that is relevant to the viral lifecycle, and to the clinical features of COVID-19. Some of the predicted drugs (e.g., tegobuvir, sonidegib, siramesine, antrafenine, bemcentinib, itacitinib, or phthalocyanine) might be suitable for repurposing to pharmacologically reactivate innate immune restriction and antagonism of SARS-CoV-2 RNAs lacking 2'-O-methylation.