Estrogen Receptor Alpha Binders for Hormone-Dependent Forms of Breast Cancer: e-QSAR and Molecular Docking Supported by X-ray Resolved Structures.
Vijay H MasandSami A Al-HussainAbdullah Y AlzahraniAamal A Al-MutairiRania A HussienAbdul SamadMagdi E A ZakiPublished in: ACS omega (2024)
Cancer, a life-disturbing and lethal disease with a high global impact, causes significant economic, social, and health challenges. Breast cancer refers to the abnormal growth of cells originating from breast tissues. Hormone-dependent forms of breast cancer, such as those influenced by estrogen, prompt the exploration of estrogen receptors as targets for potential therapeutic interventions. In this study, we conducted e-QSAR molecular docking and molecular dynamics analyses on a diverse set of inhibitors targeting estrogen receptor alpha (ER-α). The e-QSAR model is based on a genetic algorithm combined with multilinear regression analysis. The newly developed model possesses a balance between predictive accuracy and mechanistic insights adhering to the OECD guidelines. The e-QSAR model pointed out that sp 2 -hybridized carbon and nitrogen atoms are important atoms governing binding profiles. In addition, a specific combination of H-bond donors and acceptors with carbon, nitrogen, and ring sulfur atoms also plays a crucial role. The results are supported by molecular docking, MD simulations, and X-ray-resolved structures. The novel results could be useful for future drug development for ER-α.
Keyphrases
- molecular docking
- estrogen receptor
- molecular dynamics
- molecular dynamics simulations
- high resolution
- healthcare
- public health
- mental health
- density functional theory
- induced apoptosis
- gene expression
- machine learning
- papillary thyroid
- deep learning
- climate change
- cell cycle arrest
- genome wide
- current status
- cell death
- cell proliferation
- copy number
- dna methylation
- magnetic resonance imaging
- cancer therapy
- data analysis
- dual energy
- oxidative stress
- human health