9J37

Cryo-EM structure of human Alpha-7 nicotinic acetylcholine receptor

Summary for 9J37

• Entry DOI: 10.2210/pdb9j37/pdb
• EMDB information: 61108
• Descriptor: Neuronal acetylcholine receptor subunit alpha-7, 2-acetamido-2-deoxy-beta-D-glucopyranose-(1-4)-2-acetamido-2-deoxy-beta-D-glucopyranose, 2-acetamido-2-deoxy-beta-D-glucopyranose, ... (4 entities in total)
• Functional Keywords: receptors, inhibitor, membrane protein
• Biological source: Homo sapiens (human)
• Total number of polymer chains: 5
• Total formula weight: 276218.54

Authors

Yu, R.,Zhao, Y. (deposition date: 2024-08-08, release date: 2025-08-13, Last modification date: 2026-08-26)

Primary citation

Zhang, J.,Yin, Z.,Li, Y.,Ge, C.,Zhang, Z.,Yuan, P.,Jiang, T.,Craik, D.J.,Zhao, Y.,Yu, R.
Deep learning-driven discovery and mechanism of action study of a minimalist conopeptide targeting alpha 7 nicotinic acetylcholine receptor.
Acta Pharm Sin B, 16:4147-4165, 2026

PubMed Abstract: Despite extensive structural and functional characterization of the 7 nicotinic acetylcholine receptor, valuable structural insights into its interactions with conopeptides remain limited, thereby hindering the rational development of peptide-based modulators for this clinically important receptor subtype. Here, we present an integrated pipeline combining deep learning, structural biology, computational modeling and electrophysiology to accelerate the discovery and optimization of 7 nAChR-targeting conopeptides. To overcome data scarcity, we developed a deep learning model using the ESM-2 protein language framework, enabling efficient screening of 689 disulfide-poor conopeptides. This approach identified SS1, a novel antagonist of 7 nAChR, which was systematically optimized structure-activity relationship studies to yield [ΔQP,S8R]SS1-a minimalist peptide with nanomolar potency (IC = 49.2 nmol/L), enhanced selectivity, and improved stability. Cryo-EM and computational modeling resolved the 3.3 Å resolution structure of 7 nAChR bound to [S8R]SS1, revealing a unique binding mode stabilized by hydrogen bonds, hydrophobic interactions, and glycan contacts, while hybrid receptor conformations (closed/desensitized) elucidated its inhibitory mechanism. This work establishes a transformative deep learning-to-experiment framework for accelerating the discovery and optimization of nature-inspired peptide therapeutics.

PubMed: 42453416
DOI: 10.1016/j.apsb.2025.12.035

Experimental method

ELECTRON MICROSCOPY (3.3 Å)