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Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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Combining Chemical Cross-linking and Mass Spectrometry of Intact Protein Complexes to Study the Architecture of Multi-subunit Protein Assemblies

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A Roadmap for Improving Reliability and Data Sharing in Crosslinking Mass Spectrometry.

Juri Rappsilber¹, James Bruce², Colin Combe¹

¹Technische Universität Berlin, Chair of Bioanalytics, Berlin, Germany; Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK; Si-M/""Der Simulierte Mensch"", a Science Framework of Technische Universität Berlin and Charité - Universitätsmedizin Berlin, Berlin, Germany.

Molecular & Cellular Proteomics : MCP

|June 28, 2025

Summary

Crosslinking mass spectrometry (MS) offers insights into protein interactions and structures. Harmonizing data formats and error control is crucial for reliable crosslinking MS data in structural biology.

Keywords:

crosslinking mass spectrometry data analysis data sharing repository standardization

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Area of Science:

Biochemistry
Structural Biology
Proteomics

Background:

Crosslinking mass spectrometry (MS) is a powerful technique for studying protein-protein interactions and protein structures within cellular contexts.
Current limitations include inconsistent data formats, variable error control methods, and poor interoperability with data repositories, hindering widespread adoption.
Despite these challenges, recent advancements in false discovery rate (FDR) models and pipeline benchmarking demonstrate the potential for high reliability in crosslinking MS data.

Purpose of the Study:

To highlight the challenges and barriers in the field of crosslinking mass spectrometry.
To propose practical next steps for community-driven harmonization.
To emphasize the importance of standardized practices for the acceptance and dependability of crosslinking MS data.

Main Methods:

This perspective reviews recent advances in crosslinking MS, focusing on error estimation and data analysis.
It discusses the need for standardized data formats and submission protocols for public repositories.
The authors frame practical strategies for field-wide agreement on best practices.

Main Results:

Recent progress in FDR models and benchmarking indicates that crosslinking MS data can achieve reliability suitable for integrative structural biology.
The field faces persistent challenges in data standardization, error control, and repository integration.
Community consensus on error estimation, open data formats, and repository submission is essential.

Conclusions:

Harmonization of crosslinking MS practices is critical for advancing integrative structural biology.
Standardized protocols will enhance the acceptance and trustworthiness of crosslinking MS data across the scientific community.
Addressing current barriers will ensure the dependability of crosslinking MS data regardless of its origin.