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<p> Huntington’s disease (HD) is a progressive neurodegenerative disorder caused by mutations in the huntingtin (HTT) gene, leading to transcriptional dysregulation, mitochondrial dysfunction, and neuronal loss. Sirtuin 1 (SIRT 1), a NAD<sup>+</sup>-dependent deacetylase, can be neuroprotective in gene expression. Selective SIRT1 inhibition may restore transcriptional balance, making it a potential therapeutic strategy. To identify natural product-derived SIRT1 inhibitors with potential therapeutic relevance for HD. Selisistat (EX-527), a selective SIRT1 inhibitor, was used as a reference to retrieve 1401 structurally similar compounds (Tanimoto similarity ≥68%) from the LOTUS natural products database. Drug-likeness and ADMET properties were evaluated, followed by molecular docking against the catalytic domain of SIRT1 (PDB ID: 4I5I). Top hits underwent 100 ns molecular dynamics (MD) simulations in GROMACS 2023.3, and binding free estimation via MM/PBSA. Three compounds, LTS0217483, LTS0173725, and LTS0193492, showed higher binding affinities than Selisistat. LTS0217483 had the most favourable total binding energy (-183.52&#xa0;kJ/mol) and maintained stable interactions with key catalytic residues. Hydrogen bond persistence analysis demonstrated consistent ligand-protein contacts. An integrated computational approach identified LTS0217483 as a promising natural SIRT1 inhibitor for potential HD treatment. Future work will focus on compound optimisation, quantitative structure-activity relationship (QSAR) modelling, and experimental validation in cellular and animal HD models. A computerised drug discovery system has identified potential SIRT1 inhibitors that can treat Huntington’s disease. The Tanimoto was used to search for the similarity of 276,518 natural products from the LOTUS database in the presence of Selisistat as a reference, and 1401 compounds were retrieved. An in-silico method to predict AMDET properties was used, and the molecules that satisfied Lipinski’s rule were found to be the lead set. These were also excluded from the BBB permeability, Brenk, and PAINS filters. Subsequently, molecular docking and molecular dynamics simulations were performed, and MM/PBSA binding free energy calculations were conducted to validate the thermodynamic stability of the protein-ligand complexes. The compound LTS0217483 was chosen because it has the best potential inhibitor, as it has a good binding capability and a more stable configuration that could be a future drug target.</p> Graphical Abstract <p></p>

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In Silico Identification of Natural SIRT1 Inhibitors through Molecular Docking, Dynamics Simulation, and MM/PBSA

  • 期刊论文

  • 发表日期：

    2025年9月8日

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  • Shefali Kardam,
  • Pravir Kumar

摘要

Huntington’s disease (HD) is a progressive neurodegenerative disorder caused by mutations in the huntingtin (HTT) gene, leading to transcriptional dysregulation, mitochondrial dysfunction, and neuronal loss. Sirtuin 1 (SIRT 1), a NAD+-dependent deacetylase, can be neuroprotective in gene expression. Selective SIRT1 inhibition may restore transcriptional balance, making it a potential therapeutic strategy. To identify natural product-derived SIRT1 inhibitors with potential therapeutic relevance for HD. Selisistat (EX-527), a selective SIRT1 inhibitor, was used as a reference to retrieve 1401 structurally similar compounds (Tanimoto similarity ≥68%) from the LOTUS natural products database. Drug-likeness and ADMET properties were evaluated, followed by molecular docking against the catalytic domain of SIRT1 (PDB ID: 4I5I). Top hits underwent 100 ns molecular dynamics (MD) simulations in GROMACS 2023.3, and binding free estimation via MM/PBSA. Three compounds, LTS0217483, LTS0173725, and LTS0193492, showed higher binding affinities than Selisistat. LTS0217483 had the most favourable total binding energy (-183.52 kJ/mol) and maintained stable interactions with key catalytic residues. Hydrogen bond persistence analysis demonstrated consistent ligand-protein contacts. An integrated computational approach identified LTS0217483 as a promising natural SIRT1 inhibitor for potential HD treatment. Future work will focus on compound optimisation, quantitative structure-activity relationship (QSAR) modelling, and experimental validation in cellular and animal HD models. A computerised drug discovery system has identified potential SIRT1 inhibitors that can treat Huntington’s disease. The Tanimoto was used to search for the similarity of 276,518 natural products from the LOTUS database in the presence of Selisistat as a reference, and 1401 compounds were retrieved. An in-silico method to predict AMDET properties was used, and the molecules that satisfied Lipinski’s rule were found to be the lead set. These were also excluded from the BBB permeability, Brenk, and PAINS filters. Subsequently, molecular docking and molecular dynamics simulations were performed, and MM/PBSA binding free energy calculations were conducted to validate the thermodynamic stability of the protein-ligand complexes. The compound LTS0217483 was chosen because it has the best potential inhibitor, as it has a good binding capability and a more stable configuration that could be a future drug target.

Graphical Abstract