Microbial biomolecule-driven identification of next-gen GSK-3β inhibitors for brain disorders.

Ophiocordyceps sinensis

Cognition & nervesEnergy & fatigueRespiratory

SpeciesCordyceps, Caterpillar Fungus

JournalComputational biology and chemistry

Year2026

Key points

Health conditions of neurological disorders, including Alzheimer's, schizophrenia, and bipolar disorder, are associated with abnormalities in glycogen synthase kinase-3β (GSK-3β), an essential enzyme that plays a role in neurological growth, neural adaptation, and regulating mood
This research employs an in-silico methodology that utilises molecular docking, ADMET, post-docking MM-GBSA, DFT, MD simulation, post-simulation MM-GBSA, Principal component analysis, and DCCM to identify GSK-3β inhibitors derived from natural products
From 36,395 natural compounds, the top ten with significant GSK-3β binding affinities were identified, and ADMET analysis prioritised three promising molecules: CID_139587391 (NPA015418), CID_11306254 (NPA016078), and CID_57333567 (NPA014273)
Each of the compounds interacted with important GSK-3β amino acid residues, such as ALA 83, CYS 199, ILE 62, VAL 70, LEU 188, TYR 134, LEU 132, and VAL 135, suggesting that the ligands attach to the protein's usual active site
Molecular dynamics (MD) simulations at 100 ns revealed that CID_139587391 (NPA015418) exhibited the highest stability among the three compounds when bound to the target protein
PCA analysis revealed that CID_139587391 (NPA015418) forms the most stable and well-defined complex, showing compact clustering

Metadata-grounded summary

Citation abstract

Health conditions of neurological disorders, including Alzheimer's, schizophrenia, and bipolar disorder, are associated with abnormalities in glycogen synthase kinase-3β (GSK-3β), an essential enzyme that plays a role in neurological growth, neural adaptation, and regulating mood. This research employs an in-silico methodology that utilises molecular docking, ADMET, post-docking MM-GBSA, DFT, MD simulation, post-simulation MM-GBSA, Principal component analysis, and DCCM to identify GSK-3β inhibitors derived from natural products. From 36,395 natural compounds, the top ten with significant GSK-3β binding affinities were identified, and ADMET analysis prioritised three promising molecules: CID_139587391 (NPA015418), CID_11306254 (NPA016078), and CID_57333567 (NPA014273). Each of the compounds interacted with important GSK-3β amino acid residues, such as ALA 83, CYS 199, ILE 62, VAL 70, LEU 188, TYR 134, LEU 132, and VAL 135, suggesting that the ligands attach to the protein's usual active site. The binding free energies for the prioritised compounds were calculated as -37.79, -41.98, and -27.06 kcal/mol, respectively. Molecular dynamics (MD) simulations at 100 ns revealed that CID_139587391 (NPA015418) exhibited the highest stability among the three compounds when bound to the target protein. Post-simulation MM-GBSA analysis further validated its stable binding to the target protein. PCA analysis revealed that CID_139587391 (NPA015418) forms the most stable and well-defined complex, showing compact clustering. In contrast, CID_11306254 (NPA016078) displayed higher flexibility, while CID_57333567 (NPA014273) and the apoprotein showed moderate stability. In this study, Pinophilin C (CID_139587391), derived from Cordyceps gracilioides, exhibited exceptional stability within the protein's binding site throughout the analysis, confirming its potential as the most promising GSK-3β inhibitor.

Citation

Islam L, Sarker H, Amin MA, Tabassum R, Nandi A, Islam T, et al. (2026). Microbial biomolecule-driven identification of next-gen GSK-3β inhibitors for brain disorders. Computational biology and chemistry https://doi.org/10.1016/j.compbiolchem.2025.108860 PMID: 41435766

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