ArticleCancer medicine2024
Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach.
Article in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Computational discovery of marine natural phytochemicals as novel SIRT7 inhibitors for cancer treatment.Journal, genetic engineering & biotechnology · 2026Article
- Integrating traditional and modern approaches for comprehensive pharmacophore map validation in drug discovery.Journal of computer-aided molecular design · 2026Review
- Transcriptomics analysis unveils the complex interplay between diabetes and hypertension in regulating renal cell carcinoma pathway followed by pancreatic metastasis.Journal, genetic engineering & biotechnology · 2025Article
- Exploring Cadaba fruticosa bioactive compounds for oral cancer an in silico and in vitro approach.Scientific reports · 2025Article
- Phytoconstituents as emerging therapeutics for breast cancer: Mechanistic insights and clinical implications.Cancer pathogenesis and therapy · 2025Review
- Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach.Cancer medicine · 2024Article
- Enhancing cancer care with improved checkpoint inhibitors: a focus on PD-1/PD-L1.EXCLI journal · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBreast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth. OBJECTIVES/
aimsThis study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model.
methodsThis study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP.
resultsThe virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis.
conclusionBased on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.
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