Despite extensive structural and functional characterization of the α7 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 α7 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 α7 nAChR, which was systematically optimized via structure-activity relationship studies to yield [△QP,S8R]SS1—a minimalist peptide with nanomolar potency (IC₅₀ = 49.2 nmol/L), enhanced selectivity, and improved stability. Cryo-EM and computational modeling resolved the 3.3 Å resolution structure of α7 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.

Deep learning  /  Conopeptides  /  α7 nicotinic acetylcholine receptor  /  Structure–activity relationship studies  /  Cryo-EM  /  Computational modeling  /  Structure optimization  /  Molecular dynamic simulations