PRESnovo: Prescreening Prior to de novo Sequencing to Improve Accuracy and Sensitivity of Neuropeptide Identification.

Abstract

Identification of peptides in species lacking fully sequenced genomes is challenging due to the lack of prior knowledge. De novo sequencing is the method of choice, but its performance is less than satisfactory due to algorithmic bias and interference in complex MS/MS spectra. The task becomes even more challenging for endogenous peptides that do not involve an enzymatic digestion step, such as neuropeptides. However, many neuropeptides possess common sequence motifs that are conserved across members of the same family. Taking advantage of this feature to improve de novo sequencing of neuropeptides, we have developed a method named PRESnovo (prescreening precursors prior to de novo sequencing) to predict the motif from a MS/MS spectrum. A neuropeptide sequence is broken into a motif with conserved amino acid residues and the remaining partial sequence. By searching against a predefined motif database constructed from known homologous sequences, PRESnovo assigns the most probable motif to each precursor via a sophisticated scoring function. Performance analysis was conducted with 15 neuropeptide standards, and 11 neuropeptides were correctly identified with PRESnovo compared to 1 identification by PEAKS only. We applied PRESnovo to assign motifs to peptide sequences in conjunction with PEAKS for assigning the rest of the peptide sequence in order to discover neuropeptides in tissue samples of green crab, C. maenas, and Jonah crab, C. borealis. Collectively, a large number of neuropeptides were identified, including 13 putative neuropeptides identified in green crab brain, 77 in Jonah crab brain, and 47 in Jonah crab sinus glands for the first time. This PRESnovo strategy greatly simplifies de novo sequencing and enhances the accuracy and sensitivity of neuropeptide identification when common motifs are present.