In Silico Virtual Screening of Marine Aldehyde Derivatives from Seaweeds against SARS-CoV-2.
Nalae KangSeong-Yeong HeoSeon-Heui ChaGinnae AhnSoo-Jin HeoPublished in: Marine drugs (2022)
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.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- molecular docking
- human health
- machine learning
- coronavirus disease
- molecular dynamics simulations
- risk assessment
- clinical trial
- randomized controlled trial
- deep learning
- big data
- protein protein
- systematic review
- artificial intelligence
- oxidative stress
- emergency department
- molecular dynamics
- small molecule
- virtual reality
- density functional theory
- data analysis