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Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic
Sirajul Islam1, Md Al Amin1, Kannan R R Rengasamy2
1Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh.
Background:
Breast 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/Aims:
This 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.
Methods:
This 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.
Results:
The 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.
Conclusion:
Based 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.
Insights
This study identified ZINC05925939 as a promising estrogen receptor beta (ESR2) inhibitor for breast cancer treatment. Further wet lab evaluation is needed to confirm its efficacy.
Area of Science:
- Biochemistry
- Structural Biology
- Computational Chemistry
Background:
- Estrogen receptor beta (ESR2) mutations are linked to breast cancer progression.
- Understanding ESR2 mutations is crucial for developing targeted therapies.
Purpose of the Study:
- To identify shared pharmacophoric regions in ESR2 mutant proteins.
- To discover potential therapeutic targets for ESR2-mutated breast cancer.
Main Methods:
- Established a shared feature pharmacophore (SFP) model for mutant ESR2 proteins.
- Performed virtual screening of a drug library against the SFP model.
- Conducted molecular docking and dynamics simulations for selected compounds.
Main Results:
- Identified 33 potential drug candidates with high pharmacophoric fit scores.
- ZINC05925939 exhibited strong binding affinity (-10.80 kcal/mol) to wild-type ESR2.
- Molecular dynamics simulations confirmed the stability of ZINC05925939.
Conclusions:
- ZINC05925939 is a promising ESR2 inhibitor candidate.
- Further experimental validation is required to assess its therapeutic potential.
