Back

Structure-guided allosteric modulation of the delta opioid receptor

Mobbs, J. I.; Nguyen, M. D.; Deo, O.; Bartuzi, D.; Venugopal, H.; Alvi, S.; Pham, V.; Barnes, N.; Christopoulos, A.; Poole, D. P.; Carbone, S. E.; Jorg, M.; Capuano, B.; Carlsson, J.; Gondin, A. B.; Scammells, P. J.; Valant, C.; Thal, D. M.

2025-10-17 pharmacology and toxicology
10.1101/2025.10.16.682975 bioRxiv
Show abstract

Opioid analgesics remain essential for pain management but are associated with significant adverse effects, including respiratory depression, tolerance, and dependence. The {delta}-opioid receptor ({delta}OR) represents a promising therapeutic target for developing safer opioid analgesics with reduced adverse effects compared to conventional -opioid receptor-targeting drugs. Positive allosteric modulators (PAMs) offer advantages over direct agonists by enhancing endogenous opioid signaling while preserving natural spatiotemporal activation patterns, potentially avoiding tolerance and dependence issues. Here, we present high-resolution cryo-EM structures of {delta}OR complexed with the peptide agonist DADLE and the PAM MIPS3614, revealing a novel lipid-facing allosteric binding site formed by transmembrane helices 2, 3, and 4. MIPS3614 stabilizes the active receptor conformation through a critical hydrogen bond with residue N1313.35 in the conserved sodium binding site, a key regulatory region controlling GPCR activation. Comprehensive mutagenesis, molecular dynamics simulations, and structure-activity relationships validate this proposed mechanism. Structure-guided optimization yielded MIPS3983 with enhanced binding affinity and retained cooperativity. Our findings establish the first molecular framework for {delta}OR allosteric modulation and provide a structural foundation for the rational design of safer opioid therapeutics.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.