Sarsasapogenin, a spirostanol sapogenin with reported activity against ERα-positive breast cancer cells, has no defined molecular target, and its metabolic fate has not been considered in computational studies of this compound class. Network pharmacology, biotransformation profiling, and multi-level molecular modeling were combined to address both questions. Of 108 targets shared between Sarsasapogenin and breast cancer, ERα gave the most favorable docking energy among ten hub proteins (-10.46 kcal/mol), against -8.73 kcal/mol for the reference modulator Bazedoxifene. BioTransformer predicted 16 metabolites, of which five phase-I derivatives retaining the spirostanol scaffold bound ERα within a narrow window (-10.01 to -10.30 kcal/mol). The representative metabolite BTM00010, a 6-hydroxylated derivative, retained affinity at -10.30 kcal/mol and formed a hydrogen bond to Val533 that is absent from the parent pose. Over 100 ns of simulation, all three complexes reached comparable plateaus in RMSD (0.20-0.30 nm) and radius of gyration (1.68-1.80 nm), and MMGBSA ranked them in the same order as docking (-47.30, -21.48, and -11.83 kcal/mol). Post-dynamics analysis showed lower collective-motion amplitude for the metabolite complex than for the parent, and a correlated-motion network closer to the parent than to the reference modulator. Predicted phase-I hydroxylation, therefore, does not abolish ERα engagement, which argues for evaluating biotransformation products alongside the parent compound in computational screening of plant sapogenins.
S. D. Thuong, T. Từ, N. Nguyễn et al.· Journal of Molecular Graphic...· 0 citations
Breast cancer remains a major cause of morbidity and mortality in women, with around 2.3 million new cases and 670,000 deaths worldwide in 2022. Daidzin, a soy isoflavone glycoside from Glycine max, is a candidate bioactive scaffold, but its breast cancer-relevant mechanisms remain poorly defined. This study used an integrated in silico strategy combining network pharmacology and molecular modeling to prioritize daidzin targets and validate key interactions, with sirtinol as a reference compound. Target prediction identified 101 putative daidzin targets, and intersection with breast cancer-associated genes yielded 97 common targets. Protein-protein interaction analysis highlighted hub genes including ALB, TNF, MMP9, CASP3, SRC, ITGB1, MMP2, ESR1, IL2, and HSP90AA1. Enrichment analyses suggested convergence on extracellular/vesicle-related functions, metallopeptidase activity, and pathway modules spanning metabolism, inflammation, endocrine signaling, and cancer circuitry. Docking against ten hub proteins produced binding energies from −6.00 to −11.49 kcal/mol, with the strongest affinity for MMP9 (6ESM; −11.49 kcal/mol), exceeding B9Z (−10.54 kcal/mol) and sirtinol (−10.59 kcal/mol). Molecular dynamics simulations indicated stable complexes, and Molecular Mechanics Generalized Born Surface Area (MMGBSA) supported stronger binding for daidzin-MMP9 (−46.86 ± 3.83 kcal/mol) than sirtinol-MMP9 (−14.12 ± 8.99 kcal/mol). Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) prediction indicated favorable safety-related flags for daidzin, although lower predicted intestinal absorption and Caco2 permeability than sirtinol suggest potential exposure-related limitations. Density Functional Theory (DFT) analysis supported comparatively greater electronic stability. Collectively, the results prioritize a daidzin-MMP9 axis for experimental validation.
Lan Thị Vũ, L. Vu, Lien Thi Kim Vu et al.· PLoS ONE· 0 citations
Integrated modeling prioritizes CPD1 as a Bcl-2-targeting scaffold for lung cancer-relevant studies, supporting structure-guided optimization and experimental verification.
H. Nguyen· Biophysical Bulletin· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
