EndoGenius: Optimized Neuropeptide Identification from Mass Spectrometry Datasets.

Neuropeptides represent a unique class of signaling molecules that have garnered much attention but require special consideration when identifications are gleaned from mass spectra. With highly variable sequence lengths, neuropeptides must be analyzed in their endogenous state. Further, neuropeptides share great homology within families, differing by as little as a single amino acid residue, complicating even routine analyses and necessitating optimized computational strategies for confident and accurate identifications. We present EndoGenius, a database searching strategy designed specifically for elucidating neuropeptide identifications from mass spectra by leveraging optimized peptide-spectrum matching approaches, an expansive motif database, and a novel scoring algorithm to achieve broader representation of the neuropeptidome and minimize reidentification. This work describes an algorithm capable of reporting more neuropeptide identifications at 1% false-discovery rate than alternative software in five Callinectes sapidus neuronal tissue types.

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.