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Targeting SARS-CoV-2 with Chaga mushroom: An in silico study toward developing a natural antiviral compound

Summary

This study used computer modeling to investigate whether Chaga mushroom components can bind to and potentially block the coronavirus spike protein that SARS-CoV-2 uses to infect cells. The researchers found that three active compounds in Chaga—beta glycan, betulinic acid, and galactomannan—attached strongly to the virus’s binding sites in ways similar to known antiviral molecules. Beyond blocking viral entry, Chaga also has immune-boosting and anti-inflammatory properties that could help prevent the dangerous cytokine storm associated with severe COVID-19.

Background

SARS-CoV-2 has caused global outbreaks with significant mortality. The virus enters host cells through spike protein interaction with ACE-2 receptors. Chaga mushroom has demonstrated traditional use as an anti-inflammatory and immune booster with reported antiviral properties.

Objective

To assess the potential binding interactions of Chaga mushroom components with SARS-CoV-2 receptor-binding domain using molecular docking, molecular dynamics simulation, and phylogenetic analysis to develop natural antiviral therapeutics.

Results

All three Chaga components exhibited strong binding interactions with S1-carboxy-terminal domain (binding energies: -7.4 to -8.6 kcal/mol), comparable to control molecule NAG (-8.67 kcal/mol). Key interacting residues included TRP-436, ASN-437, ASN-440, and others involved in ACE-2 binding. Phylogenetic analysis revealed the presence of furin cleavage site (NSPRRA) in recent SARS-CoV-2 isolates, absent in previous variants.

Conclusion

Chaga mushroom components demonstrate significant binding affinity for SARS-CoV-2 spike protein and could potentially inhibit viral entry while providing immune-boosting and anti-inflammatory benefits. These findings suggest Chaga as a promising natural therapeutic candidate for supplementing current anti-SARS-CoV-2 treatments.
  • Published in:Food Science & Nutrition,
  • Study Type:In silico computational study,
  • Source: PMID: 34900242, DOI: 10.1002/fsn3.2576
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