Integrated in silico profiling of Moringa oleifera leaf constituents as collagenase and elastase inhibitors: drug-likeness, pharmacophore–similarity ranking and MD Simulation

Collagenase (matrix metalloproteinase-1, MMP-1) and elastase drive the degradation of dermal collagen and elastin and are validated targets for anti-ageing cosmeceuticals. Moringa oleifera leaf extract inhibits both enzymes, but the contribution of its individual constituents has not been profiled on a common computational footing. We assembled a panel of twelve compounds ten M. oleifera leaf constituents (quercetin, kaempferol, gallic acid, chlorogenic acid, caffeic acid, ferulic acid, quinic acid, rutin, isoquercitrin, astragalin) and two reference inhibitors (EGCG, ascorbic acid) and evaluated them in a four-tier workflow: (i) RDKit-based physicochemical descriptors with Lipinski, Veber and BOILED-Egg-type classification; (ii) SMARTS-based pharmacophore feature analysis and Morgan-fingerprint similarity, combined into a pharmacophore–similarity score (PSS); (iii) structure-based molecular docking against collagenase (PDB 1CGL) and elastase (PDB 1U4G); and (iv) 100 ns all-atom molecular dynamics (GROMACS) of the top-ranked MMP-1 complexes against the EGCG reference. Seven of twelve compounds showed no Lipinski violations; the flavonol O-glycosides and EGCG were predicted to have low gastrointestinal absorption. Chlorogenic acid ranked first among native constituents by PSS (10.00) and was the best-scoring ligand at elastase (−8.93 kcal/mol), ahead of quercetin (−7.74 kcal/mol) and the ascorbic acid reference (−6.52 kcal/mol); vicenin-2 scored −6.52 kcal/mol at collagenase against −5.96 kcal/mol for EGCG. All four simulated complexes remained stable (backbone RMSD 0.21–0.27 nm, Rg ≈ 2.30 nm, SASA ≈ 200 nm2), with quercetin forming the most persistent hydrogen bonds. Chlorogenic acid, quercetin and gallic acid emerge as priority candidates for enzyme-inhibition assays and topical formulation work; all findings are computational predictions.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-28
DOI
https://doi.org/10.5281/zenodo.23728402
Primary Topic
Computational Drug Discovery Methods
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article
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article

Integrated in silico profiling of Moringa oleifera leaf constituents as collagenase and elastase inhibitors: drug-likeness, pharmacophore–similarity ranking and MD Simulation

Ankita Mishra, Rohit Sahu, Frank Deshmukh, Mahima Turkar
3 links
Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
article

Integrated in silico profiling of Moringa oleifera leaf constituents as collagenase and elastase inhibitors: drug-likeness, pharmacophore–similarity ranking and MD Simulation

Ankita Mishra, Rohit Sahu, Frank Deshmukh, Mahima Turkar
article en

Abstract

Collagenase (matrix metalloproteinase-1, MMP-1) and elastase drive the degradation of dermal collagen and elastin and are validated targets for anti-ageing cosmeceuticals. Moringa oleifera leaf extract inhibits both enzymes, but the contribution of its individual constituents has not been profiled on a common computational footing. We assembled a panel of twelve compounds ten M. oleifera leaf constituents (quercetin, kaempferol, gallic acid, chlorogenic acid, caffeic acid, ferulic acid, quinic acid, rutin, isoquercitrin, astragalin) and two reference inhibitors (EGCG, ascorbic acid) and evaluated them in a four-tier workflow: (i) RDKit-based physicochemical descriptors with Lipinski, Veber and BOILED-Egg-type classification; (ii) SMARTS-based pharmacophore feature analysis and Morgan-fingerprint similarity, combined into a pharmacophore–similarity score (PSS); (iii) structure-based molecular docking against collagenase (PDB 1CGL) and elastase (PDB 1U4G); and (iv) 100 ns all-atom molecular dynamics (GROMACS) of the top-ranked MMP-1 complexes against the EGCG reference. Seven of twelve compounds showed no Lipinski violations; the flavonol O-glycosides and EGCG were predicted to have low gastrointestinal absorption. Chlorogenic acid ranked first among native constituents by PSS (10.00) and was the best-scoring ligand at elastase (−8.93 kcal/mol), ahead of quercetin (−7.74 kcal/mol) and the ascorbic acid reference (−6.52 kcal/mol); vicenin-2 scored −6.52 kcal/mol at collagenase against −5.96 kcal/mol for EGCG. All four simulated complexes remained stable (backbone RMSD 0.21–0.27 nm, Rg ≈ 2.30 nm, SASA ≈ 200 nm2), with quercetin forming the most persistent hydrogen bonds. Chlorogenic acid, quercetin and gallic acid emerge as priority candidates for enzyme-inhibition assays and topical formulation work; all findings are computational predictions.

Zenodo (CERN European Organization for Nuclear Research)
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Computational Drug Discovery Methods
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