Back

An engineered nanobody inhibitor for molecular-to-circuit control of opioid receptor function

Valbret, Z.; Lamothe-Molina, P. L.; Papadogkonaki, S.; Koehl, A.; Arafiles, J. V. V.; Shapiro Tuchman, Z.; Fisher, N. M.; Radoux-Mergault, A.; Canziani, A.; Huang, G.; Von Zastrow, M.; Kosloff, M.; Hackenberger, C. P.; Manglik, A.; Patriarchi, T.; Stoeber, M.

2026-01-14 neuroscience
10.64898/2026.01.12.698943 bioRxiv
Show abstract

Opioid receptors (ORs) orchestrate pain relief, reward, and dependence, yet their signaling arises from diverse cell types and subcellular compartments that cannot be selectively interrogated with existing pharmacological or genetic approaches. Single-domain antibodies, or nanobodies (Nbs), can probe receptor states, but their potential as tools for controlling native receptor signaling at the system level has remained unexplored. Here, we engineer a suite of high-affinity intracellular Nbs that bind active ORs through structure-guided evolution and in silico design. Iterative optimization yields Nb64, a potent inhibitor that rapidly suppresses transducer engagement, receptor internalization, and downstream signaling, including endogenous pathways in neuronal cells. Organelle targeting highlights Nb64s capacity to control OR activity with subcellular precision, while bio-reversible cell-penetrating peptide (CPP) conjugation enables non-genetic cytosolic delivery. Cell-type-specific expression of Nb64 in VTA interneurons attenuates fentanyl-evoked dopamine release and behavioral responses in mice, demonstrating targeted control of opioid actions in vivo. Nb64 provides a versatile strategy for dissecting OR biology and establishes a generalizable framework for precision inhibition of native GPCR signaling in vivo.

Matching journals

The top 4 journals account 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.