Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was confirmed as the causative virus of COVID-19 disease, which is currently a worldwide pandemic. Efavirenz, a non-nucleoside reverse transcriptase inhibitor (NNRTI), is one of the most potent chemical compounds proposed to treat COVID-19 infection. We, therefore, performed virtual screening on FDA approved drugs that are similar to the efavirenz moiety. Subsequently, the compounds were subjected to screening by analyzing their drug-likeness, such as Lipinski's rule of five and ADMET properties. Molecular docking study revealed that Met165, His41, His163, and Phe140 were important interacting residues for COVID-19 main protease receptor-ligand interaction. Five top-ranked compounds, podophyllotoxin, oxacillin, lovastatin, simvastatin, and gefitinib, were selected by virtual screening and docking studies. The highest occupied molecular (HOMO) orbital, lowest unoccupied molecular orbital (LUMO) and energy gap values was calculated using density functional theory (DFT). The results of the study showed that lovastatin and simvastatin might be considered as lead compounds for further development for COVID-19 main protease inhibitors.
Due to the migratory flow of infected people with severe acute respiratory syndrome virus (SARS-CoV-2), the numbers of confirmed cases of coronavirus 2019 (COVID-19) infections is accelerating worldwide and pre-clinical evidence of antiviral agents that can combat this pandemic is still elusive. We identified published SAR-CoV efficacy experiments in which some selected compounds were used to test the reduction of the virus load in mice. We then developed a combined model based on scoping review, meta-analyses, and molecular docking studies to evaluate the effect size of preclinical studies of compounds that have been tested against SARS-CoV. Molecular docking studies of the inhibitors in the active pocket of COVID-19 protease were also performed. Our results identified three SARS-CoV inhibitors i.e. EIDD-2801, GS-5734 and amodiaquine that are excellent options for optimization and drug development to treat or cure COVID-19.
Noroviruses are non-enveloped viruses with a positive-sense single-stranded RNA (ssRNA) genome belonging to the genus Norovirus, from the family Caliciviridae, which are accountable for acute gastroenteritis in humans. The Norovirus genus is subdivided into seven genogroups, i.e., (GI-GVII); among these, the genogroup II and genotype 4 (GII.4) strains caused global outbreaks of human norovirus (HuNov) disease. The viral genome comprises three open reading frames (ORFs). ORF1 encodes the nonstructural polyprotein that is cleaved into six nonstructural proteins, which include 3C-like cysteine protease (3CLpro) and a viral RNA-dependent RNA polymerase. ORF2 and ORF3 encode the proteins VP1 and VP2. The RNA-dependent RNA polymerase (RdRp) from noroviruses is one of the multipurpose enzymes of RNA viruses vital for replicating and transcribing the viral genome, making the virally encoded enzyme one of the critical targets for the development of novel anti-norovirus agents. In the quest for a new antiviral agent that could combat HuNov, high throughput virtual screening (HTVS), combined with e-pharmacophore screening, was applied to screen compounds from the PubChem database. CMX521 molecule was selected as a prototype for a similarity search in the PubChem online database. Molecular dynamics simulations were employed to identify different compounds that may inhibit HuNov. The results predicted that compound CID-57930781 and CID-44396095 formed stable complexes with MNV-RdRp within 50 ns; hence, they may signify as promising human norovirus inhibitors.
Catalytic (H3VO4) oxidative dehydrogenation (ODH) mechanistic studies of the activation of n-hexane have been conducted by means of Density Functional Theory (DFT). Catalytic oxidative dehydrogenation is an important strategy for the conversion of alkanes to alkenes to provide useful chemical feedstocks from saturated hydrocarbons. Transition metal oxide catalysts based on vanadium provide an important class of catalysts for this reaction. The catalyst is usually prepared with vana-dium in a high oxidation state prepared as an over layer supported on a relatively inert main group oxide (silica, alumina, etc.). Activation of the hydrocarbon is then energetically possible through the reduction of vanadium cations which can be subsequently re-oxidised using molecular O2 to complete the catalytic cycle. The aim of this study was to use density functional theory to explore the catalytic mechanism of this type of reaction using the conversion of n-hexane to 1-and 2-hexene as an illustrative example. Calculations are performed for the 1-and 2-hexene radical pathways and the results extrapolated to discuss the expected selectivity under laboratory experimental conditions (573, 673 and 773 K). Consideration of 3-hexene is excluded as in our earlier experimental studies and in the general literature this product is not reported. The stationary points on the potential energy surfaces were characterized and the associated geometries and relative energies (E # , E, G # and G) were determined. The relative energies of all intermediates and transition states identified are used to lend insight on the mechanistic pathways for the reaction. We have concentrated on the role of the transition metal in this chemistry and so the catalyst model chosen is an isolated, tetrahedral H3VO4 cluster containing one vanadyl bond, V(V)]O. The calculated rate-limiting step is the CeH bond activation (-hydrogen abstraction) from the C6H14 chain by the vanadyl O, with a calculated E # = +27.4 kcal/mol. This produces a C6H13HOH3VO3 complex as an intermediate with vanadium reduced to V(IV). There are then two possible routes for the propagation step that leads to 2-hexene. Firstly, the abstraction of the second-hydrogen on the radical intermediate fragment (%C6H13) can take place on a diff erent active V (V) = O site. Secondly this step may involve reaction with gas-phase molecular O2. These alternatives are compared computationally and results used to discuss some existing experimental data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.