Download MendeleyDeep learning-driven discovery and mechanism of action study of a minimalist conopeptide targeting alpha 7 nicotinic acetylcholine receptor.
Zhang, J., Yin, Z., Li, Y., Ge, C., Zhang, Z., Yuan, P., Jiang, T., Craik, D.J., Zhao, Y., Yu, R.(2026) Acta Pharm Sin B 16: 4147-4165
- PubMed: 42453416 Search on PubMedSearch on PubMed Central
- DOI: https://doi.org/10.1016/j.apsb.2025.12.035
- Primary Citation Related Structures:
9J37 - PubMed Abstract:
Despite extensive structural and functional characterization of the α 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 α 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 α 7 nAChR, which was systematically optimized via structure-activity relationship studies to yield [ΔQP,S8R]SS1-a minimalist peptide with nanomolar potency (IC 50 = 49.2 nmol/L), enhanced selectivity, and improved stability. Cryo-EM and computational modeling resolved the 3.3 Å resolution structure of α 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.
- Key Laboratory of Marine Drugs, Chinese Ministry of Education, School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China.
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