Proteome-wide neuropeptide identification using NeuroPeptide-HMMer (NP-HMMer)

• Published in: General and comparative endocrinology Vol. 357; p. 114597
• Main Authors: Zandawala, Meet, Bilal Amir, Muhammad, Shin, Joel, Yim, Won C., Alfonso Yañez Guerra, Luis
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2024
• Subjects: Animals; Computational Biology - methods; Drosophila melanogaster - metabolism; Evolution; Hidden Markov model; Invertebrate; Markov Chains; Neuropeptide; Neuropeptides - analysis; Neuropeptides - genetics; Neuropeptides - metabolism; Priapulid; Proteome - metabolism; Rotifer; Invertebrate; Hidden Markov model; Evolution; Neuropeptide; Rotifer; Priapulid
• ISSN: 0016-6480; 1095-6840; 1095-6840
• DOI: 10.1016/j.ygcen.2024.114597

•NP-HMMer is an open-source tool to facilitate neuropeptide discovery.•NP-HMMer is based on hidden Markov models.•We discovered members of several neuropeptide families in Priapulida and Rotifera. Neuropeptides are essential neuronal signaling molecules that orchestrate animal behavior and physiology via actions within the nervous system and on peripheral tissues. Due to the small size of biologically active mature peptides, their identification on a proteome-wide scale poses a significant challenge using existing bioinformatics tools like BLAST. To address this, we have developed NeuroPeptide-HMMer (NP-HMMer), a hidden Markov model (HMM)-based tool to facilitate neuropeptide discovery, especially in underexplored invertebrates. NP-HMMer utilizes manually curated HMMs for 46 neuropeptide families, enabling rapid and accurate identification of neuropeptides. Validation of NP-HMMer on Drosophila melanogaster, Daphnia pulex, Tribolium castaneum and Tenebrio molitor demonstrated its effectiveness in identifying known neuropeptides across diverse arthropods. Additionally, we showcase the utility of NP-HMMer by discovering novel neuropeptides in Priapulida and Rotifera, identifying 22 and 19 new peptides, respectively. This tool represents a significant advancement in neuropeptide research, offering a robust method for annotating neuropeptides across diverse proteomes and providing insights into the evolutionary conservation of neuropeptide signaling pathways.