A Multi-Angle Approach to Predict Peptide-GPCR Complexes: The N/OFQ-NOP System as a Successful AlphaFold Application...

Area of Science:

• Computational chemistry and structural biology
• Pharmacology and drug discovery

Background:

• G protein-coupled receptors (GPCRs) have nearly 700 solved structures, making them ideal for validating structure prediction algorithms.
• Predicting active state GPCR-ligand complexes is crucial for understanding receptor function and designing therapeutics.

Purpose of the Study:

• To predict the active state complex of the nociceptin/orphanin FQ-NOP receptor (N/OFQ-NOPa) using computational approaches.
• To validate *in silico* predictions through the synthesis and pharmacological testing of novel peptide analogues.

Main Methods:

• Combined classical homology modeling and artificial intelligence (AI)-based protein modeling.
• Utilized AI-based peptide structure prediction and molecular docking.
• Designed, synthesized, and pharmacologically characterized novel N/OFQ(1-13)-NH2 analogues.

Main Results:

• Successfully predicted the N/OFQ-NOP receptor active state complex with atomistic accuracy (Cα RMSD < 1.0 Å).
• Discovered a novel NOP receptor antagonist (compound 3B) with a pKB of 6.63.
• The antagonist features a single ring-constrained residue replacing the Gly2-Gly3 motif in the N/OFQ peptide sequence.

Conclusions:

• This study demonstrates the first atomistic prediction of a peptide-GPCR complex.
• The successful modification of the N/OFQ message moiety with a rigid scaffold highlights a novel drug design strategy.
• Validated computational predictions with experimental data, leading to a new class of NOP receptor antagonists.