Thesis
IN SILICO STUDY OF ALKALOID COMPOUNDS WITH COMPUTATIONAL APPROACH FOR SELECTION OF DRUG LEADS FOR COVID-19
The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virulent source of the COVID-19 disease. As a result of the rapid transmission of the viral agent and deficiency of specific drugs against the virus, a worldwide pandemic ensued with a terrifying death toll. Thus there is tremendous urgency to discover substances for the development of specific COVID-19 drugs. With increasing public interest in natural products, this study aims to discover alkaloid compounds capable of inhibiting SARS-CoV-2 with the assistance of bioinformatics. In this work, 298 alkaloids with reported antiviral properties were identified, and its activities were validated with QSAR analysis using Pass Online server until only 7 alkaloids remained. Molecular docking studies for these 7 alkaloids onto SARS-CoV-2 3CLpro, a protein involved in viral replication, were carried out with AutoDock Vina, followed by in silico visualization of the protein-alkaloid interaction with Ligplot+ program and prediction of ADME-Tox properties of the alkaloids using Toxtree program and SwissADME online server. This study shows that fangchinoline, phenanthroindolizidine, and polyalthenol exhibited high binding affinity values to SARS-CoV-2 3CLpro, with polyalthenol predicted to possess the strongest binding interactions to the active site of the protein. However, supplementary observation of phenanthroindolizidine for the prospect of mutagenicity is required before it can be recommended for further drug development. All the alkaloids were predicted to confer high gastrointestinal absorptive probability, but fangchinoline might be a challenge to synthesize for use in future in vivo and in vitro experiments.
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