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.