POMS enhances open spectral library search for identification of modified peptides
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Updated: Nov 7, 2026

The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides
Published on: November 2, 2021
Younghee Seo1, Eunok Paek2,3, Seungjin Na4,5
1Center for Target-to-Therapeutics Research, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea.
Background:
Peptide identification from tandem mass spectrometry (MS/MS) data is a central task in proteomics. Spectral library searching combined with open modification search (OMS) has emerged as an effective strategy for identifying peptides carrying unexpected post-translational modifications (PTMs). However, existing methods do not adequately account for modification-induced fragment-ion shifts during candidate selection, leading to missed identifications when mass shifts distort spectral similarity.
Results:
We present POMS, a modification-aware framework for open spectral library search that improves candidate retrieval by integrating complementary spectral representations with sequence-derived theoretical fragment features. These representations compensate for modification-induced fragment-ion shifts and improve alignment between query and library spectra, increasing robustness to modification site variability. Benchmarking on large-scale human MS/MS datasets showed that POMS yielded up to ~ 8% more peptide identifications during the open search phase than conventional methods. Cross-validation with independent database search engines further demonstrated a > 5% increase in consistent peptide identifications. Notably, POMS substantially improved the identification of peptides carrying C-terminal modifications, for which conventional candidate retrieval methods are particularly susceptible to fragment-ion shifts.
Conclusions:
POMS improves the sensitivity and reliability of modification-tolerant spectral library searching by incorporating modification-aware candidate retrieval while preserving computational efficiency. The framework provides a practical solution for large-scale PTM discovery and is readily applicable to existing spectral library search workflows. POMS is publicly available under the CC BY-NC-SA 4.0 license at https://github.com/icp-kbsi/POMS .
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