ABSTRACT
SARS-CoV-2 currently lacks effective first-line drug treatment. We present promising data from in silico docking studies of new Methisazone compounds (modified with calcium, Ca; iron, Fe; magnesium, Mg; manganese, Mn; or zinc, Zn) designed to bind more strongly to key proteins involved in replication of SARS-CoV-2. In this in silico molecular docking study, we investigated the inhibiting role of Methisazone and the modified drugs against SARS-CoV-2 proteins: ribonucleic acid (RNA)-dependent RNA polymerase (RdRp), spike protein, papain-like protease (PlPr), and main protease (MPro). We found that the highest binding interactions were found with the spike protein (6VYB), with the highest overall binding being observed with Mn-bound Methisazone at -8.3 kcal/mol, followed by Zn and Ca at -8.0 kcal/mol, and Fe and Mg at -7.9 kcal/mol. We also found that the metal-modified Methisazone had higher affinity for PlPr and MPro. In addition, we identified multiple binding pockets that could be singly or multiply occupied on all proteins tested. The best binding energy was with Mn-Methisazone versus spike protein, and the largest cumulative increases in binding energies were found with PlPr. We suggest that further studies are warranted to identify whether these compounds may be effective for treatment and/or prophylaxis.
Subject(s)
Antiviral Agents/chemistry , Metals/chemistry , Methisazone/chemistry , Molecular Docking Simulation , SARS-CoV-2/chemistry , Antiviral Agents/metabolism , Calcium/chemistry , Calcium/metabolism , Coronavirus 3C Proteases/chemistry , Coronavirus 3C Proteases/metabolism , Coronavirus Papain-Like Proteases/chemistry , Coronavirus Papain-Like Proteases/metabolism , Coronavirus RNA-Dependent RNA Polymerase/chemistry , Coronavirus RNA-Dependent RNA Polymerase/metabolism , Drug Design , Humans , Iron/chemistry , Iron/metabolism , Magnesium/chemistry , Magnesium/metabolism , Manganese/chemistry , Manganese/metabolism , Metals/metabolism , Methisazone/metabolism , Models, Molecular , Molecular Dynamics Simulation , Protein Binding , SARS-CoV-2/metabolism , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/metabolism , Zinc/chemistry , Zinc/metabolism , COVID-19 Drug TreatmentABSTRACT
Comprehensive data mining of the scientific literature has become an increasing challenge. To address this challenge, Elsevier's Pathway Studio software uses the techniques of natural language processing to systematically extract specific biological information from journal articles and abstracts that is then used to create a very large, structured, and constantly expanding literature knowledgebase. Highly sophisticated visualization tools allow the user to interactively explore the vast number of connections created and stored in the Pathway Studio database. We demonstrate the value of this structured information approach by way of a biomarker use case example and describe a comprehensive collection of biomarkers and biomarker candidates, as reported in the literature. We use four major neuropsychiatric diseases to demonstrate common and unique biomarker elements, demonstrate specific enrichment patterns, and highlight strategies for identifying the most recent and novel reports for potential biomarker discovery. Finally, we introduce an innovative new taxonomy based on brain region identifications, which greatly increases the potential depth and complexity of information retrieval related to, and now accessible for, neuroscience research.
