PeptideMiner—neuropeptide discovery across the animal kingdom
Abstract
Neuropeptides represent the largest and most diverse class of cell-to-cell signaling molecules, holding important roles in animal physiology and behavior. They are evolutionarily ancient and widely distributed across the animal kingdom. Although over 200 neuropeptides have been identified, only a small fraction has been functionally characterized. A recognized bottleneck is the lack of effective tools to study their biological roles and therapeutic potential. Interestingly, neuropeptide-like peptides are also found in animal venoms, where they contribute to prey capture or defensive strategies. Mapping neuropeptide families across the animal kingdom is challenging due to their high sequence divergence and short mature peptide sequences. To address this, we developed PeptideMiner, a search tool that employs profile-hidden Markov models (profile-HMMs) for family-specific peptide discovery. PeptideMiner was systematically validated and benchmarked against existing methods, demonstrating its superior performance. By applying PeptideMiner to several venom transcriptomes—including 24 previously unpublished datasets—we identified 10 novel natriuretic peptides from distantly related species and 57 novel insulin-like sequences from marine predatory cone snails. Chemical synthesis and structure–activity relationship studies of newly identified conoinsulins at human insulin receptors emphasized the value of our approach in elucidating ligand–receptor interactions and discovering new pharmacological probes and therapeutic leads. PeptideMiner offers a powerful platform for discovering new bioactive peptides and family-specific analogues, accelerating both natural product discovery and evolutionary research.
Helen C Mendel, Gene Hopping, Eivind A B Undheim, Johannes Zuegg, Richard J Lewis, Briony E Forbes, Quentin Kaas, Markus Muttenthaler, PeptideMiner—neuropeptide discovery across the animal kingdom, GigaScience, Volume 14, 2025, giaf078, https://doi.org/10.1093/gigascience/giaf078