PepExplorer: A Similarity-driven Tool for Analyzing de Novo Sequencing Results

Abstract

Peptide spectrum matching is the current gold standard for protein identification via mass-spectrometry-based proteomics. Peptide spectrum matching compares experimental mass spectra against theoretical spectra generated from a protein sequence database to perform identification, but protein sequences not present in a database cannot be identified unless their sequences are in part conserved. The alternative approach, de novo sequencing, can make it possible to infer a peptide sequence directly from a mass spectrum, but interpreting long lists of peptide sequences resulting from large-scale experiments is not trivial. With this as motivation, PepExplorer was developed to use rigorous pattern recognition to assemble a list of homologue proteins using de novo sequencing data coupled to sequence alignment to allow biological interpretation of the data. PepExplorer can read the output of various widely adopted de novo sequencing tools and converge to a list of proteins with a global false-discovery rate. To this end, it employs a radial basis function neural network that considers precursor charge states, de novo sequencing scores, peptide lengths, and alignment scores to select similar protein candidates, from a target-decoy database, usually obtained from phylogenetically related species. Alignments are performed using a modified Smith-Waterman algorithm tailored for the task at hand. We verified the effectiveness of our approach using a reference set of identifications generated by ProLuCID when searching for Pyrococcus furiosus mass spectra on the corresponding NCBI RefSeq database. We then modified the sequence database by swapping amino acids until ProLuCID was no longer capable of identifying any proteins. By searching the mass spectra using PepExplorer on the modified database, we were able to recover most of the identifications at a 1% false-discovery rate. Finally, we employed PepExplorer to disclose a comprehensive proteomic assessment of the Bothrops jararaca plasma, a known biological source of natural inhibitors of snake toxins. PepExplorer is integrated into the PatternLab for Proteomics environment, which makes available various tools for downstream data analysis, including resources for quantitative and differential proteomics. Molecular & Cellular

Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0

“…Additionally, the SEProQ functionalities (XIC and Isobaric Browser) were significantly upgraded and are now integrated into the same graphical user interface. New modules, such as PepExplorer, whose functionality is similar to that of SEPro but for de novo sequencing 33 , and the XD Scoring system for phosphopeptide localization, are also part of the new version.…”

“…As for its hallmarks, we believe they lie in analyzing label-free data through the T-Fold 31 module and in the isobaric (e.g., iTRAQ/TMT) analyzer module. Some of its unique features include providing an integrated cloud service 32 , modules for statistically filtering and performing assembly of de novo sequencing data 33 , statistically scoring phosphopeptides 34 , dealing with time-course experiments 35 , and offering a module for integrated Gene Ontology analysis 36 . Modules yet to be integrated in future versions are capable of deisotoping and decharging mass spectra 18 , and of identifying cross-linked peptides to address protein-protein interaction and aid in providing structural data 37 (the latter is described in a recent protocol 38 ).…”

“…Some examples are: Búzios, which allows the clustering of similar proteomic profiles 5 ; the XD Scoring system, for evaluating the confidence in phosphosites 34 ; PepExplorer, a tool for analyzing shotgun proteomic data of unsequenced organisms 33 ; tools for performing ANOVA analysis; the incorporation of the Comet search engine, wrapped in a graphical user interface 42 , for analyzing isobaric experiments (e.g., iTRAQ and TMT); and a cloud service that enables large-scale quantitative predictions and comparisons of protein domains 32 . Some existing modules were significantly upgraded, such as the one for XICs.…”

“…To cope with these limitations, approaches stemming from de novo sequencing have emerged. Among them we highlight Spectral Networks 47 , Mod-A 48 , MS-Blast 49 , and PepExplorer 33 . The first two are capable of pinpointing unanticipated modifications while the last two start with de novo sequencing results and align them against sequence databases of homologue organisms so that similar proteins can be determined.…”

Novel Catalytically-Inactive PII Metalloproteinases from a Viperid Snake Venom with Substitutions in the Canonical Zinc-Binding Motif

“…Only de novo sequences showing ≥99% ALC (average of local confidence) were exported and submitted to sequence alignment against the mature BlatPII-c clone sequence using the algorithm PepExplorer [37]. The following parameters were used in the similarity-driven analysis: 75% minimum identity, minimum of 6 residues per peptide, substitution matrix PAM30MS.…”