Toward the Identification of Natural Antiviral Drug Candidates against Merkel Cell Polyomavirus: Computational Drug Design Approaches.

Merkel cell carcinoma (MCC) is a rare form of aggressive skin cancer mainly caused by Merkel cell polyomavirus (MCPyV). Most MCC tumors express MCPyV large T (LT) antigens and play an important role in the growth-promoting activities of oncoproteins. Truncated LT promotes tumorigenicity as well as host cell proliferation by activating the viral replication machinery, and inhibition of this protein in humans drastically lowers cellular growth linked to the corresponding cancer. Our study was designed with the aim of identifying small molecular-like natural antiviral candidates that are able to inhibit the proliferation of malignant tumors, especially those that are aggressive, by blocking the activity of viral LT protein. To identify potential compounds against the target protein, a computational drug design including molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized Born surface area (MM-GBSA) approaches were applied in this study. Initially, a total of 2190 phytochemicals isolated from 104 medicinal plants were screened using the molecular docking simulation method, resulting in the identification of the top five compounds having the highest binding energy, ranging between -6.5 and -7.6 kcal/mol. The effectiveness and safety of the selected compounds were evaluated based on ADME and toxicity features. A 250 ns MD simulation confirmed the stability of the selected compounds bind to the active site (AS) of the target protein. Additionally, MM-GBSA analysis was used to determine the high values of binding free energy (ΔG bind) of the compounds binding to the target protein. The five compounds identified by computational approaches, Paulownin (CID: 3084131), Actaealactone (CID: 11537736), Epigallocatechin 3-O-cinnamate (CID: 21629801), Cirsilineol (CID: 162464), and Lycoricidine (CID: 73065), can be used in therapy as lead compounds to combat MCPyV-related cancer. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the virus.

Spike protein recognizer receptor ACE2 targeted identification of potential natural antiviral drug candidates against SARS-CoV-2.

Angiotensin-converting enzyme 2 (ACE2), also known as peptidyl-dipeptidase A, belongs to the dipeptidyl carboxydipeptidases family has emerged as a potential antiviral drug target against SARS-CoV-2. Most of the ACE2 inhibitors discovered till now are chemical synthesis; suffer from many limitations related to stability and adverse side effects. However, natural, and selective ACE2 inhibitors that possess strong stability and low side effects can be replaced instead of those chemicals' inhibitors. To envisage structurally diverse natural entities as an ACE2 inhibitor with better efficacy, a 3D structure-based-pharmacophore model (SBPM) has been developed and validated by 20 known selective inhibitors with their correspondence 1166 decoy compounds. The validated SBPM has excellent goodness of hit score and good predictive ability, which has been appointed as a query model for further screening of 11,295 natural compounds. The resultant 23 hits compounds with pharmacophore fit score 75.31 to 78.81 were optimized using in-silico ADMET and molecular docking analysis. Four potential natural inhibitory molecules namely D-DOPA (Amb17613565), L-Saccharopine (Amb6600091), D-Phenylalanine (Amb3940754), and L-Mimosine (Amb21855906) have been selected based on their binding affinity (-7.5, -7.1, -7.1, and -7.0 kcal/mol), respectively. Moreover, 250 ns molecular dynamics (MD) simulations confirmed the structural stability of the ligands within the protein. Additionally, MM/GBSA approach also used to support the stability of molecules to the binding site of the protein that also confirm the stability of the selected four natural compounds. The virtual screening strategy used in this study demonstrated four natural compounds that can be utilized for designing a future class of potential natural ACE2 inhibitor that will block the spike (S) protein dependent entry of SARS-CoV-2 into the host cell.

Pharmacophore-Based Virtual Screening, Quantum Mechanics Calculations, and Molecular Dynamics Simulation Approaches Identified Potential Natural Antiviral Drug Candidates against MERS-CoV S1-NTD.

Middle East respiratory syndrome coronavirus (MERS-CoV) is a highly infectious zoonotic virus first reported into the human population in September 2012 on the Arabian Peninsula. The virus causes severe and often lethal respiratory illness in humans with an unusually high fatality rate. The N-terminal domain (NTD) of receptor-binding S1 subunit of coronavirus spike (S) proteins can recognize a variety of host protein and mediates entry into human host cells. Blocking the entry by targeting the S1-NTD of the virus can facilitate the development of effective antiviral drug candidates against the pathogen. Therefore, the study has been designed to identify effective antiviral drug candidates against the MERS-CoV by targeting S1-NTD. Initially, a structure-based pharmacophore model (SBPM) to the active site (AS) cavity of the S1-NTD has been generated, followed by pharmacophore-based virtual screening of 11,295 natural compounds. Hits generated through the pharmacophore-based virtual screening have re-ranked by molecular docking and further evaluated through the ADMET properties. The compounds with the best ADME and toxicity properties have been retrieved, and a quantum mechanical (QM) based density-functional theory (DFT) has been performed to optimize the geometry of the selected compounds. Three optimized natural compounds, namely Taiwanhomoflavone B (Amb23604132), 2,3-Dihydrohinokiflavone (Amb23604659), and Sophoricoside (Amb1153724), have exhibited substantial docking energy >-9.00 kcal/mol, where analysis of frontier molecular orbital (FMO) theory found the low chemical reactivity correspondence to the bioactivity of the compounds. Molecular dynamics (MD) simulation confirmed the stability of the selected natural compound to the binding site of the protein. Additionally, molecular mechanics generalized born surface area (MM/GBSA) predicted the good value of binding free energies (ΔG bind) of the compounds to the desired protein. Convincingly, all the results support the potentiality of the selected compounds as natural antiviral candidates against the MERS-CoV S1-NTD.