In Silico Virtual Screening of Marine Aldehyde Derivatives from Seaweeds against SARS-CoV-2

利用计算机模拟虚拟筛选海藻中海洋醛衍生物对抗SARS-CoV-2病毒

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Abstract

Coronavirus disease 2019, caused by the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global pandemic that poses an unprecedented threat to the global economy and human health. Several potent inhibitors targeting SARS-CoV-2 have been published; however, most of them have failed in clinical trials. This study aimed to assess the therapeutic compounds among aldehyde derivatives from seaweeds as potential SARS-CoV-2 inhibitors using a computer simulation protocol. The absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties of the compounds were analyzed using a machine learning algorithm, and the docking simulation of these compounds to the 3C-like protease (Protein Data Bank (PDB) ID: 6LU7) was analyzed using a molecular docking protocol based on the CHARMm algorithm. These compounds exhibited good drug-like properties following the Lipinski and Veber rules. Among the marine aldehyde derivatives, 4-hydroxybenzaldehyde, 3-hydroxybenzaldehyde, 3,4-dihydroxybenzaldehyde, and 5-bromoprotocatechualdehyde were predicted to have good absorption and solubility levels and non-hepatotoxicity in the ADME/Tox prediction. 3-hydroxybenzaldehyde and 3,4-dihydroxybenzaldehyde were predicted to be non-toxic in TOPKAT prediction. In addition, 3,4-dihydroxybenzaldehyde was predicted to exhibit interactions with the 3C-like protease, with binding energies of -71.9725 kcal/mol. The computational analyses indicated that 3,4-dihydroxybenzaldehyde could be regarded as potential a SARS-CoV-2 inhibitor.

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