Inhibition Ability of Natural Compounds on Receptor-Binding Domain of SARS-CoV2: An In Silico Approach

Computer-Aided Drug Design and Synthesis of Rhenium Clotrimazole Antimicrobial Agents

In the context of the global health issue caused by the growing occurrence of antimicrobial resistance (AMR), the need for novel antimicrobial agents is becoming alarming. Inorganic and organometallic complexes represent a relatively untapped source of antibiotics. Here, we report a computer-aided drug design (CADD) based on a 'scaffold-hopping' approach for the synthesis and antibacterial evaluation of fac-Re(I) tricarbonyl complexes bearing clotrimazole (ctz) as a monodentate ligand. The prepared molecules were selected following a pre-screening in silico analysis according to modification of the 2,2'-bipyridine (bpy) ligand in the coordination sphere of the complexes. CADD pointed to chiral 4,5-pinene and 5,6-pinene bipyridine derivatives as the most promising candidates. The corresponding complexes were synthesized, tested toward methicillin-sensitive and -resistant S. aureus strains, and the obtained results evaluated with regard to their binding affinity with a homology model of the S. aureus MurG enzyme. Overall, the title species revealed very similar minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values as those of the reference compound used as the scaffold in our approach. The obtained docking scores advocate the viability of 'scaffold-hopping' for de novo design, a potential strategy for more cost- and time-efficient discovery of new antibiotics.

The Development of Pharmacophore Models for the Search of New Natural Inhibitors of SARS-CoV-2 Spike RBD-ACE2 Binding Interface

To date, some succeeding variants of SARS-CoV-2 have become more contagious. This virus is known to enter human cells by binding the receptor-binding domain (RBD) of spike protein with the angiotensin-converting enzyme 2 (ACE2), the latter being a membrane protein that regulates the renin-angiotensin system. Since the host cell receptor plays a critical role in viral entry, inhibition of the RBD-ACE2 complex is a promising strategy for preventing COVID-19 infection. In the present communication, we propose and utilize an approach based on the generation of a complex of pharmacophore models and subsequent Induced Fit Docking (IFD) to identify potential inhibitors of the main binding sites of the Omicron SARS-CoV-2 RBD(S1)-ACE2 complex (PDB ID: 7T9L) among a number of natural products of various types and origins. Several natural compounds have been found to provide a high affinity for the receptor of interest. It is expected that the present results will stimulate further research aimed at the development of specialized drugs against this virus.

Insights from computational studies on the potential of natural compounds as inhibitors against SARS-CoV-2 spike omicron variant

Coronavirus disease 2019 (COVID-19) is a major global health emergency, with more than six million deaths worldwide. It is becoming increasingly challenging to treat COVID-19 due to the emergence of novel variants. The omicron variant is capable to evade defences and spread quickly. Among many validated COVID-19 targets, the spike (S) protein plays an important role in receptor recognition (via the S1 subunit) and membrane fusion (via the S2 subunit). The S protein is one of the vital targets for the development of drugs to combat this illness. In this research, we applied various computational methods such as molecular docking, molecular dynamics, MM-GBSA calculations, and ADMET prediction to identify potential natural products from Saudi medicinal plants against the spike omicron variant. As a result, three compounds (LTS0002490, LTS0117007, and LTS0217912) were identified with better binding affinity to the spike omicron variant compared to the reference compound (VE607). In addition, these compounds showed stable interactions with the target during molecular dynamics simulations for 140 ns. Last, these compounds have optimal ADMET properties. We suggest that these compounds may be considered promising hits to treat COVID-19 if experimentally validated.

Discovery of natural products to block SARS-CoV-2 S-protein interaction with Neuropilin-1 receptor: A molecular dynamics simulation approach

Neuropilin-1 (NRP1) is a widely expressed cell surface receptor protein characterized by its pleiotropic function. Recent reports highlighted NRP1 as an additional entry point of the SARS-CoV-2 virus, enhancing viral infectivity by interacting with the S-protein of SARS-CoV-2. The ubiquitous distribution and mechanism of action of NRP1 enable the SARS-CoV-2 virus to attack multiple organs in the body simultaneously. Therefore, blocking NRP1 is a potential therapeutic approach against SARS-CoV-2 infection. The current study screened the South African natural compounds database (SANCDB) for molecules that can disrupt the SARS-CoV-2 S protein-NRP1 interaction as a potential antiviral target for SARS-CoV-2 cellular entry. Following excessive screening and validation analysis 3-O-Methylquercetin and Esculetin were identified as potential compounds to disrupt the S-protein-NRP1 interaction. Furthermore, to understand the conformational stability and dynamic features between NRP1 interaction with the selected natural products, we performed 200 ns molecular dynamics (MD) simulations. In addition, molecular mechanics-generalized Born surface area (MM/GBSA) was utilized to calculate the free binding energies of the natural products interacting with NRP1. 3-O-methylquercetin showed an inhibitory effect with binding energies ΔG of -25.52 ± 0.04 kcal/mol to NRP1, indicating the possible disruption of the NRP1-S-protein interaction. Our analysis demonstrated that 3-O-methylquercetin presents a potential antiviral compound against SARS-CoV-2 infectivity. These results set the path for future functional in-vitro and in-vivo studies in SARS-CoV-2 research.

Identification of Novel Inhibitor of Enoyl-Acyl Carrier Protein Reductase (InhA) Enzyme in Mycobacterium tuberculosis from Plant-Derived Metabolites: An In Silico Study

Mycobacterium tuberculosis (M.tb.) enoyl-acyl carrier protein (ACP) reductase (InhA) is validated as a useful target for tuberculosis therapy and is considered an attractive enzyme to drug discovery. This study aimed to identify the novel inhibitor of the InhA enzyme, a potential target of M.tb. involved in the type II fatty acid biosynthesis pathway that controls mycobacterial cell envelope synthesis. We compiled 80 active compounds from Ruta graveolens and citrus plants belonging to the Rutaceae family for pharmacokinetics and molecular docking analyses. The chemical structures of the 80 phytochemicals and the 3D structure of the target protein were retrieved from the PubChem database and RCSB Protein Data Bank, respectively. The evaluation of druglikeness was performed based on Lipinski’s Rule of Five, while the computed phytochemical properties and molecular descriptors were used to predict the ADMET of the compounds. Amongst these, 11 pharmacokinetically-screened compounds were further examined by performing molecular docking analysis with an InhA target using AutoDock 4.2. The docking results showed that gravacridonediol, a major glycosylated natural alkaloid from Ruta graveolens, might possess a promising inhibitory potential against InhA, with a binding energy (B.E.) of −10.80 kcal/mole and inhibition constant (Ki) of 600.24 nM. These contrast those of the known inhibitor triclosan, which has a B.E. of −6.69 kcal/mole and Ki of 12.43 µM. The binding efficiency of gravacridonediol was higher than that of the well-known inhibitor triclosan against the InhA target. The present study shows that the identified natural compound gravacridonediol possesses drug-like properties and also holds promise in inhibiting InhA, a key target enzyme of M.tb.

Discovery of Small Molecules from Echinacea angustifolia Targeting RNA-Dependent RNA Polymerase of Japanese Encephalitis Virus

The Japanese encephalitis virus (JEV), a mosquito-borne flavivirus that causes viral encephalitis leading to neural damage, is a major threat in most Asian countries. The RNA-dependent RNA polymerase (RdRp) present in the viral genome is the key component for genome replication, making it an attractive target for antiviral drug development. In this study, the natural products from Echinacea angustifolia were retrieved for structure-based virtual screening against JEV-RdRp. The top six compounds (Echinacoside, Echinacin, Rutin, Cynaroside, Quercetagetin 7-glucoside, and Kaempferol-3-glucoside) were obtained based on the highest negative docking score, ADMET (absorption, distribution, metabolism, excretion, and toxicity), and molecular interaction. The computational analysis of these selected compounds against the co-crystallized ligands, i.e., ATP and GTP, were performed. Further, 100 ns molecular dynamic simulation and post-free binding energy calculation of all the selected compounds complexed with JEV-RdRP were performed to check the stability of the complexes. The obtained results showed considerable stability and intermolecular interaction with native ligand-binding site residues of JEV-RdRp. Hence, selected natural compounds are admissible inhibitors of JEV-RdRp protein and can be considered for future antiviral drug development studies.

Development of Machine-Learning Model to Predict COVID-19 Mortality: Application of Ensemble Model and Regarding Feature Impacts

This study was designed to develop machine-learning models to predict COVID-19 mortality and identify its key features based on clinical characteristics and laboratory tests. For this, deep-learning (DL) and machine-learning (ML) models were developed using receiver operating characteristic (ROC) area under the curve (AUC) and F1 score optimization of 87 parameters. Of the two, the DL model exhibited better performance (AUC 0.8721, accuracy 0.84, and F1 score 0.76). However, we also blended DL with ML, and the ensemble model performed the best (AUC 0.8811, accuracy 0.85, and F1 score 0.77). The DL model is generally unable to extract feature importance; however, we succeeded by using the Shapley Additive exPlanations method for each model. This study demonstrated both the applicability of DL and ML models for classifying COVID-19 mortality using hospital-structured data and that the ensemble model had the best predictive ability.

Lactoferrin Inhibition of the Complex Formation between ACE2 Receptor and SARS CoV-2 Recognition Binding Domain

The present investigation focuses on the analysis of the interactions among human lactoferrin (LF), SARS-CoV-2 receptor-binding domain (RBD) and human angiotensin-converting enzyme 2 (ACE2) receptor in order to assess possible mutual interactions that could provide a molecular basis of the reported preventative effect of lactoferrin against CoV-2 infection. In particular, kinetic and thermodynamic parameters for the pairwise interactions among the three proteins were measured via two independent techniques, biolayer interferometry and latex nanoparticle-enhanced turbidimetry. The results obtained clearly indicate that LF is able to bind the ACE2 receptor ectodomain with significantly high affinity, whereas no binding to the RBD was observed up to the maximum “physiological” lactoferrin concentration range. Lactoferrin, above 1 µM concentration, thus appears to directly interfere with RBD–ACE2 binding, bringing about a measurable, up to 300-fold increase of the K_D value relative to RBD–ACE2 complex formation.