Document Type

Article

Publication Date

3-29-2023

Abstract

The rapid spread of the coronavirus disease 2019 (COVID-19) resulted in serious health, social, and economic consequences. While the development of effective vaccines substantially reduced the severity of symptoms and the associated deaths, we still urgently need effective drugs to further reduce the number of casualties associated with SARS-CoV-2 infections. Machine learning methods both improved and sped up all the different stages of the drug discovery processes by performing complex analyses with enormous datasets. Natural products (NPs) have been used for treating diseases and infections for thousands of years and represent a valuable resource for drug discovery when combined with the current computation advancements. Here, a dataset of 406,747 unique NPs was screened against the SARS-CoV-2 main protease (Mpro) crystal structure (6lu7) using a combination of ligand- and structural-based virtual screening. Based on 1) the predicted binding affinities of the NPs to the Mpro, 2) the types and number of interactions with the Mpro amino acids that are critical for its function, and 3) the desirable pharmacokinetic properties of the NPs, we identified the top 20 candidates that could potentially inhibit the Mpro protease function. A total of 7 of the 20 top candidates were subjected to in vitro protease inhibition assay and 4 of them (4/7; 57%), including two beta carbolines, one N-alkyl indole, and one Benzoic acid ester, had significant inhibitory activity against Mpro protease. These four NPs could be developed further for the treatment of COVID-19 symptoms.

Comments

This article was originally published in Biology, volume 12, issue 4, in 2023. https://doi.org/10.3390/biology12040519

This scholarship is part of the Chapman University COVID-19 Archives.

biology-12-00519-s001.zip (175 kB)
Supplemental materials. Table S1: The classification of the 431 NP ligands identified with both high probability of binding and an increased likelihood of high oral absorption. Table S2: The top 81 NPs with ≤−8 kcal/mol binding affinity. Table S3: The classification of the top 81 NPs. Table S4: Chemcial classification of the top 20 NPs. Table S5: Information about the seven compounds tested in vitro.

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