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PTMs_Closed_Search: Multiple Post-Translational Modification Closed Search Using Reduced Search Space and Transferred

Yury Yu Strogov1, Sergey A Spirin2,3,4, Mark V Ivanov5

  • 1Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow 119991, Russia.

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|February 20, 2026
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Summary
This summary is machine-generated.

This study introduces an optimized Python pipeline for post-translational modification (PTM) analysis in MS/MS data, improving peptide identification and protein coverage. The new method enhances false discovery rate (FDR) estimation for more reliable PTM detection.

Keywords:
algorithmsclosed searchgroup-specific FDRpost-translational modification

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Area of Science:

  • Proteomics
  • Computational Biology
  • Bioinformatics

Background:

  • Current post-translational modification (PTM) searches in MS/MS data use either open modification search (OMS) or closed search (CS) algorithms.
  • OMS allows for many PTMs and unknown mass-shifts, while CS is more sensitive but limited in PTM scope.
  • A need exists for more comprehensive and sensitive PTM identification methods.

Purpose of the Study:

  • To develop an optimized Python algorithm for automated, sequential PTM searching.
  • To enhance the significance of false discovery rate (FDR) estimation for PTM analysis.
  • To improve peptide identification and protein coverage in MS/MS data.

Main Methods:

  • An optimized Python algorithm based on the IdentiPy search engine was developed for automated sequential PTM searching.
  • The algorithm utilizes public databases and custom protein lists for PTM annotation.
  • Modified FDR calculation with spline approximation and error propagation was implemented for data filtration.

Main Results:

  • The pipeline demonstrated comparable results to existing methods for unmodified peptides and proteins.
  • Thirteen different types of peptide PTMs were identified, increasing relative protein coverage.
  • The spline-based FDR filtration method yielded a superior number of identified peptides compared to separate FDR calculations.

Conclusions:

  • The developed pipeline offers a robust solution for multiple PTM searches in MS/MS data.
  • It can be employed as a standalone application or integrated into existing data analysis platforms.
  • The method enhances PTM identification accuracy and efficiency.