Functional Food to Therapeutic Lead: Structure‐Based Molecular Docking and Simulation Reveal Anti‐Inflammatory Potential of Toddy ‐Derived Metabolites Targeting SerpinB4

ABSTRACT Toddy , a naturally fermented traditional beverage, is increasingly recognized not only as a cultural staple but also as a reservoir of diverse bioactive metabolites. However, the molecular mechanisms by which these compounds interact with host proteins involved in inflammation remain unclear. Earlier studies largely focused on metabolite profiling or basic in vitro assays, leaving a gap in structure‐based validation, molecular dynamics analysis, and pharmacokinetic assessment. To address this, the present study employed an integrated in silico approach combining molecular docking, long‐timescale molecular dynamics simulations, and ADMET prediction to evaluate the potential interactions of toddy ‐derived metabolites with inflammation‐associated protein targets relevant to gut inflammation. Using untargeted LC–MS/MS data, 520 metabolites were screened against 22 inflammation‐associated proteins known to be upregulated. Pfaffic acid emerged as a leading candidate and exhibited a favorable predicted binding affinity toward SerpinB4 (−10.0 kcal/mol), which was lower than that predicted for the reference drug etrasimod (−9.4 kcal/mol) under the computational conditions used. Interaction analysis revealed extensive hydrogen bonding, hydrophobic interactions, π–π stacking, and halogen contacts within the SerpinB4 binding pocket. These results were supported by 200 ns molecular dynamics simulations, demonstrating improved structural stability, reduced residue flexibility, enhanced compactness, and more favorable binding free energy.

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Journal
ChemistrySelect
Published
2026-10-29
DOI
https://doi.org/10.1002/slct.74990
Primary Topic
Computational Drug Discovery Methods
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article
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article

Functional Food to Therapeutic Lead: Structure‐Based Molecular Docking and Simulation Reveal Anti‐Inflammatory Potential of Toddy ‐Derived Metabolites Targeting SerpinB4

Jyoti Prakash Tamang, Anil Kumar Verma, Rohit Das, Buddhiman Tamang et al.
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ChemistrySelect
Computational Drug Discovery Methods
article

Functional Food to Therapeutic Lead: Structure‐Based Molecular Docking and Simulation Reveal Anti‐Inflammatory Potential of Toddy ‐Derived Metabolites Targeting SerpinB4

Jyoti Prakash Tamang, Anil Kumar Verma, Rohit Das, Buddhiman Tamang, Souvik Das
article en

Abstract

ABSTRACT Toddy , a naturally fermented traditional beverage, is increasingly recognized not only as a cultural staple but also as a reservoir of diverse bioactive metabolites. However, the molecular mechanisms by which these compounds interact with host proteins involved in inflammation remain unclear. Earlier studies largely focused on metabolite profiling or basic in vitro assays, leaving a gap in structure‐based validation, molecular dynamics analysis, and pharmacokinetic assessment. To address this, the present study employed an integrated in silico approach combining molecular docking, long‐timescale molecular dynamics simulations, and ADMET prediction to evaluate the potential interactions of toddy ‐derived metabolites with inflammation‐associated protein targets relevant to gut inflammation. Using untargeted LC–MS/MS data, 520 metabolites were screened against 22 inflammation‐associated proteins known to be upregulated. Pfaffic acid emerged as a leading candidate and exhibited a favorable predicted binding affinity toward SerpinB4 (−10.0 kcal/mol), which was lower than that predicted for the reference drug etrasimod (−9.4 kcal/mol) under the computational conditions used. Interaction analysis revealed extensive hydrogen bonding, hydrophobic interactions, π–π stacking, and halogen contacts within the SerpinB4 binding pocket. These results were supported by 200 ns molecular dynamics simulations, demonstrating improved structural stability, reduced residue flexibility, enhanced compactness, and more favorable binding free energy.

ChemistrySelectVol. 11(41)
Sikkim University (IN)
Good health and well-being
Openalex Percentile: Top 14%
Computational Drug Discovery Methods
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