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Related Experiment Video

Updated: Nov 19, 2025

The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides

The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides

Published on: November 2, 2021

Enhancing Open Modification Searches via a Combined Approach Facilitated by Ursgal.

Stefan Schulze¹, Aime Bienfait Igiraneza¹, Manuel Kösters²

¹Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.

Journal of Proteome Research

|January 30, 2021

Summary

Combining open modification search (OMS) engines significantly boosts peptide identification in proteomics. This approach enhances the discovery and quantification of peptidoforms, advancing our understanding of post-translational modifications (PTMs).

Keywords:

bioinformatics glycosylation open modification search post-translational modifications proteomics python

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Published on: October 8, 2017

Area of Science:

Proteomics
Biochemistry
Computational Biology

Background:

Accurate identification of peptide sequences and post-translational modifications (PTMs) is vital for bottom-up proteomics.
Open modification search (OMS) engines enable comprehensive PTM analysis, improving peptide spectrum matching and peptidoform identification.

Purpose of the Study:

To compare and combine results from multiple OMS engines for enhanced proteomics data analysis.
To establish a unified approach for processing and analyzing OMS results, including PTM mapping and quantification.

Main Methods:

Comparison and integration of results from three distinct OMS engines.
Evaluation of OMS engine performance in identifying glycosylated peptides.
Implementation within the Python framework Ursgal for streamlined analysis.

Main Results:

Combining OMS engines increased peptide spectrum matches by 8-18%.
Unified search results facilitated downstream processing, including PTM mapping.
Demonstrated the capability of OMS engines to identify glycosylated peptides.

Conclusions:

Combining OMS engines offers a significant advantage for high-throughput proteomics.
A unified approach to OMS results enhances the depth and breadth of PTM and peptidoform identification.
The Ursgal framework simplifies the application of OMS for large-scale proteomic data analysis.