Multiple-targeting and conformational selection in the estrogen receptor: computation and experiment

Area of Science:

• Computational chemistry and molecular modeling
• Biomolecular recognition and drug discovery
• Endocrinology and receptor pharmacology

Background:

• Conformational selection is key in how biological molecules recognize each other.
• Understanding how drugs interact with different protein conformations is crucial for drug development.
• Existing docking methods struggle to model complex interactions involving multiple receptor states.

Purpose of the Study:

• To develop and test a computational protocol for selecting drug candidates based on their binding to distinct protein conformational states.
• To investigate the binding of ligands to estrogen receptor alpha (ERα) and estrogen receptor beta (ERβ) in various conformations.
• To identify novel estrogen receptor subtype-selective agonists.

Main Methods:

• A novel computational protocol combining conformational selection with docking score analysis.
• Testing the protocol on estrogen receptor α (ERα) and estrogen receptor β (ERβ) systems.
• Experimental validation using a yeast-based reporter gene system to assess ligand function.

Main Results:

• The computational protocol successfully inferred the functional outcome of ligand binding by assessing synergistic binding to distinct conformational states.
• Calculated docking scores indicated the ability of ligands to bind simultaneously to both ERα and ERβ in agonist and antagonist conformations.
• Several phytoestrogens were identified as potential novel estrogen receptor β selective agonists.

Conclusions:

• The proposed computational protocol effectively predicts estrogen receptor subtype selectivity and ligand function.
• This method offers an improved approach for discovering selective agonists compared to traditional models.
• The findings highlight the potential of specific phytoestrogens as targeted therapeutics for ERβ.