Structure-Based Prediction of G-Protein-Coupled Receptor Ligand Function: A β-Adrenoceptor Case Study

Area of Science:

• Computational chemistry and structural biology
• Drug discovery and medicinal chemistry
• G-protein-coupled receptor (GPCR) research

Background:

• Advances in GPCR structure determination enable structure-based ligand discovery.
• Predicting GPCR ligand function (agonist, antagonist, inverse agonist) from structure remains challenging.

Purpose of the Study:

• To explore the possibilities and limitations of structure-based prediction of GPCR ligand function.
• To investigate the impact of protein conformation and interaction fingerprints (IFPs) on selective virtual screening (VS) for GPCR ligands.

Main Methods:

• Utilized 31 β1 and β2 adrenoceptor crystal structures with various bound ligands.
• Applied protein-ligand interaction fingerprints (IFPs) to post-process docking poses of known ligands and decoys.
• Analyzed 1920 unique IFP-structure combinations to assess their effect on VS enrichment.

Main Results:

• Ligands with the same function can be efficiently classified using their protein-ligand interaction profiles.
• Small differences in receptor conformation and IFP scoring significantly impact VS selectivity.
• Selective enrichment of agonists and antagonists/inverse agonists was achieved using optimal structure-IFP combinations.

Conclusions:

• Protein-ligand interaction fingerprints are crucial for scoring docking poses in selective GPCR virtual screening.
• Optimal structure-IFP combinations can identify and discriminate between different GPCR ligand functions.
• This study provides a framework for structure-based discovery of GPCR ligands with specific functional outcomes.