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Molecules (Basel, Switzerland) Nov 2020Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is...
Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is the causative agent of the novel human coronavirus disease 2019 (COVID-19). Almost from the beginning of the COVID-19 outbreak several attempts were made to predict possible drugs capable of inhibiting the virus replication. In the present work a drug repurposing study is performed to identify potential SARS-CoV-2 protease inhibitors. We created a Quantitative Structure-Activity Relationship (QSAR) model based on a machine learning strategy using hundreds of inhibitor molecules of the main protease (M) of the SARS-CoV coronavirus. The QSAR model was used for virtual screening of a large list of drugs from the DrugBank database. The best 20 candidates were then evaluated in-silico against the M of SARS-CoV-2 by using docking and molecular dynamics analyses. Docking was done by using the Gold software, and the free energies of binding were predicted with the MM-PBSA method as implemented in AMBER. Our results indicate that levothyroxine, amobarbital and ABP-700 are the best potential inhibitors of the SARS-CoV-2 virus through their binding to the M enzyme. Five other compounds showed also a negative but small free energy of binding: nikethamide, nifurtimox, rebimastat, apomine and rebastinib.
Topics: Amobarbital; Antiviral Agents; Binding Sites; Computer Simulation; Coronavirus 3C Proteases; Drug Discovery; Drug Repositioning; Humans; Machine Learning; Molecular Docking Simulation; Molecular Dynamics Simulation; Pandemics; Protease Inhibitors; Protein Binding; Quantitative Structure-Activity Relationship; SARS-CoV-2; Small Molecule Libraries; Software; Thermodynamics; Thyroxine; COVID-19 Drug Treatment
PubMed: 33172092
DOI: 10.3390/molecules25215172 -
Bioorganic & Medicinal Chemistry Jul 2020HMG-CoA reductase (HMGCR) is a rate-limiting enzyme in the cholesterol biosynthetic pathway, and its catalytic domain is the well-known target of cholesterol-lowering...
HMG-CoA reductase (HMGCR) is a rate-limiting enzyme in the cholesterol biosynthetic pathway, and its catalytic domain is the well-known target of cholesterol-lowering drugs, statins. HMGCR is subject to layers of negative feedback loops; excess cholesterol inhibits transcription of the gene, and lanosterols and oxysterols accelerate degradation of HMGCR. A class of synthetic small molecules, bisphosphonate esters exemplified by SR12813, has been known to induce accelerated degradation of HMGCR and reduce the serum cholesterol level. Although genetic and biochemical studies revealed that the accelerated degradation requires the membrane domain of HMGCR and Insig, an oxysterol sensor on the endoplasmic reticulum membrane, the direct target of the bisphosphonate esters remains unclear. In this study, we developed a potent photoaffinity probe of the bisphosphonate esters through preliminary structure-activity relationship study and demonstrated binding of the bisphosphonate esters to the HMGCR membrane domain. These results provide an important clue to understand the elusive mechanism of the SR12813-mediated HMGCR degradation and serve as a basis to develop more potent HMGCR degraders that target the non-catalytic, membrane domain of the enzyme.
Topics: Cells, Cultured; Diphosphonates; Dose-Response Relationship, Drug; HEK293 Cells; Humans; Hydroxymethylglutaryl CoA Reductases; Hydroxymethylglutaryl-CoA Reductase Inhibitors; Molecular Structure; Structure-Activity Relationship
PubMed: 32616181
DOI: 10.1016/j.bmc.2020.115576