Extended template-based modeling and evaluation method using consensus of binding mode of GPCRs for virtual screening

Area of Science:

• Biochemistry and Structural Biology
• Computational Chemistry
• Pharmacology

Background:

• G-protein-coupled receptors (GPCRs) are crucial drug targets due to their role in numerous physiological processes.
• Recent advances in GPCR structural determination enable structure-based virtual screening (SBVS) for identifying active ligands.
• Accurate 3D models are essential for GPCRs with unknown structures, posing challenges in template selection and binding site diversity.

Purpose of the Study:

• To develop and validate an extended template-based modeling method for enhanced SBVS.
• To address challenges in accurate GPCR modeling and understanding ligand-binding site diversity.
• To generate reliable GPCR models for effective virtual screening in drug discovery.

Main Methods:

• Developed a novel workflow combining fragmental and standard template-based modeling for GPCR structure generation.
• Validated model reliability using virtual screening tests with known ligands and decoys.
• Assessed binding mode consensus via protein-ligand interaction fingerprint (PLIF) from docking simulations.

Main Results:

• Generated reliable 3D models for GPCR targets with both known and unknown structures.
• Achieved high ligand selectivity and consensus binding modes for the modeled GPCRs.
• Successfully applied the workflow to human dopamine receptor 3, histamine H1 receptor, delta opioid receptor, and serotonin 2A receptor.

Conclusions:

• The developed modeling method enhances the accuracy and reliability of GPCR structures for SBVS.
• This approach facilitates the identification of potent ligands for pharmaceutically important GPCR targets.
• The validated workflow offers a robust strategy for structure-based drug design against GPCRs.