ArticleGigaScience2025
PeptideMiner-neuropeptide discovery across the animal kingdom.
Article in GigaScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- PeptideMiner-neuropeptide discovery across the animal kingdom.GigaScience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
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 By applying PeptideMiner to several venom transcriptomes-including 24 previously unpublished datasets-we identified 10 novel natriuretic peptides from distantly related species and 57 novel insulin-like sequences from marine predatory cone snails.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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.