Deep learning-driven discovery and mechanism of action study of a minimalist conopeptide targeting α7 nicotinic acetylcholine receptor

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. AI-powered pipeline for discovering, optimizing, and elucidating the action mechanism of natural conopeptides targeting α 7 nAChR.