Direct mapping of Peptide-to-Spectra-Matches to genome information facilitates qualifying proteomics information

Abstract

Background: Small Proteins have received increasing attention in recent years. They have in particular been implicated as signals contributing to the coordination of bacterial communities. In genome annotations they are often missing or hidden among large numbers of hypothetical proteins because genome annotation pipelines often exclude short open reading frames or over-predict hypothetical proteins based on simple models. The validation of novel proteins, and in particular of small proteins (sProteins), therefore requires additional evidence. Proteogenomics is considered the gold standard for this purpose. It extends beyond established annotations and includes all possible open reading frames (ORFs) as potential sources of peptides, thus allowing the discovery of novel, unannotated proteins. Typically this results in large numbers of putative novel small proteins fraught with large fractions of false-positive predictions. Results: We observe that number and quality of the Peptide-to-Spectra-Matches (PSMs) that map to a candidate ORF can be highly informative for the purpose of distinguishing proteins from spurious ORF annotations. We report here on a workflow that aggregates PSM quality information and local context into simple descriptors and reliably separates likely proteins from the large pool of false-positive, i.e., most likely untranslated ORFs. We investigated the artificial gut microbiome model SIHUMIx, comprising eight different species, for which we validate 5114 proteins that previously have been annotated only as hypothetical ORFs. In addition, we identified 37 non-annotated protein candidates for which we found evidence in proteomic and transcriptomic level. Half (19) of these candidates have close functional homologs in other species. Another 12 candidates have homologs designated as hypothetical proteins in other species. The remaining six candidates are short (< 100 AA) and are most likely bona fide novel proteins. Conclusions: The aggregation of PSM quality information for predicted ORFs provides a robust and efficient method to identify novel proteins in proteomics data. The workflow is in particular also capable of identifying small proteins and frameshift variants. Since PSMs are explicitly mapped to genomic locations, it furthermore facilitates the integration with transcriptomics data and other source of genome-level information.

A workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locations

“…Two strategies are used to compute the FDR prot , the "classical" and the "picked" strategy (see [29] and the "Methods" section). The regression lines extrapolating to large value of ŝ , for which the decoy database produced no hits, is computed from the ŝ-score interval For all figures we provided interactive plots which can be found under [36]. There each individual PSM/ dot can be inspected and mapped to its corresponding candidate.…”

“…The genomes and corresponding annotations used for the project are all publicly available by The NCBI Assembly database [52] a full list can be found in Additional file 1: Table S2. The following material is available for download from [53]: • SIHUMIx track hub (track hub for the UCSC genome browser) • Result web page (full list of candidates, ecoli annotation errors and interactive plots) • Validation hash map (Maps each annotated protein in SIHUMIx to a validation level as of the time of the publication) All scripts which are used to generated the data for this publication are available under [54].…”