HyPep: An Open-Source Software for Identification and Discovery of Neuropeptides Using Sequence Homology Search

Neuropeptides are a class of endogenous peptides that have key regulatory roles in biochemical, physiological, and behavioral processes. Mass spectrometry analyses of neuropeptides often rely on protein informatics tools for database searching and peptide identification. As neuropeptide databases are typically experimentally built and comprised of short sequences with high sequence similarity to each other, we developed a novel database searching tool, HyPep, which utilizes sequence homology searching for peptide identification. HyPep aligns de novo sequenced peptides, generated through PEAKS software, with neuropeptide database sequences and identifies neuropeptides based on the alignment score. HyPep performance was optimized using LC-MS/MS measurements of peptide extracts from various Callinectes sapidus neuronal tissue types and compared with a commercial database searching software, PEAKS DB. HyPep identified more neuropeptides from each tissue type than PEAKS DB at 1% false discovery rate, and the false match rate from both programs was 2%. In addition to identification, this report describes how HyPep can aid in the discovery of novel neuropeptides.

Automated summary

Neuropeptides play important regulatory roles in biochemical, physiological, and behavioral processes. This study introduces HyPep, a novel database searching tool that utilizes sequence homology searching for neuropeptide identification. The performance of HyPep was optimized and compared with a commercial database searching software, PEAKS DB, using LC-MS/MS measurements. HyPep identified more neuropeptides than PEAKS DB at a 1% false discovery rate and both programs had a false match rate of 2%. The report also highlights how HyPep can aid in the discovery of novel neuropeptides.