HyPep: An Open-Source Software for Identification and Discovery of Neuropeptides Using Sequence Homology Search
peer-reviewed · Journal of Proteome Research · 2023
| Date | 2023-02-03 |
| Type | peer-reviewed |
| Venue | Journal of Proteome Research |
| Publisher | American Chemical Society (ACS) |
| Contribution | adjacent |
| DOI | 10.1021/acs.jproteome.2c00597 |
| Citations (OpenAlex) | 10 |
| Venue 2-year citedness | 3.83 |
Abstract
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.
Methods and tools
- HyPep: Open-source tool that aligns PEAKS de novo sequences against neuropeptide databases with sequence-homology scoring to identify known and discover new neuropeptides.
Methods it uses
- PEAKS: Commercial DP-based de novo