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Enhancing open modification searches via a combined approach facilitated by Ursgal

The identification of peptide sequences and their post-translational modifications (PTMs) is a crucial step in the analysis of bottom-up proteomics data. The recent development of open modification search (OMS) engines allows virtually all PTMs to be searched for. This not only increases the number...

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Main Authors: Schulze, Stefan (Author) , Igiraneza, Aime Bienfait (Author) , Kösters, Manuel (Author) , Leufken, Johannes (Author) , Leidel, Sebastian A. (Author) , Garcia, Benjamin A. (Author) , Fufezan, Christian (Author) , Pohlschroder, Mechthild (Author)
Format: Article (Journal)
Language:English
Published: January 29, 2021
In: Journal of proteome research
Year: 2021, Volume: 20, Issue: 4, Pages: 1986-1996
ISSN:1535-3907
DOI:10.1021/acs.jproteome.0c00799
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1021/acs.jproteome.0c00799
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Author Notes:Stefan Schulze, Aime Bienfait Igiraneza, Manuel Kösters, Johannes Leufken, Sebastian A. Leidel, Benjamin A. Garcia, Christian Fufezan, and Mechthild Pohlschroder
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Summary:The identification of peptide sequences and their post-translational modifications (PTMs) is a crucial step in the analysis of bottom-up proteomics data. The recent development of open modification search (OMS) engines allows virtually all PTMs to be searched for. This not only increases the number of spectra that can be matched to peptides but also greatly advances the understanding of the biological roles of PTMs through the identification, and the thereby facilitated quantification, of peptidoforms (peptide sequences and their potential PTMs). Whereas the benefits of combining results from multiple protein database search engines have been previously established, similar approaches for OMS results have been missing so far. Here we compare and combine results from three different OMS engines, demonstrating an increase in peptide spectrum matches of 8-18%. The unification of search results furthermore allows for the combined downstream processing of search results, including the mapping to potential PTMs. Finally, we test for the ability of OMS engines to identify glycosylated peptides. The implementation of these engines in the Python framework Ursgal facilitates the straightforward application of the OMS with unified parameters and results files, thereby enabling yet unmatched high-throughput, large-scale data analysis.
Item Description:Gesehen am 12.05.2021
Physical Description:Online Resource
ISSN:1535-3907
DOI:10.1021/acs.jproteome.0c00799

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