Mono- and Intralink Filter (Mi-Filter) To Reduce False Identifications in Cross-Linking Mass Spectrometry Data

Area of Science:

• Systems structural biology
• Biochemistry
• Proteomics

Background:

• Cross-linking mass spectrometry (XL-MS) is crucial for understanding protein-protein interactions (PPIs).
• Accurate assessment of inter-protein cross-links is vital for reliable PPI identification.
• Current XL-MS workflows can yield false-positive inter-protein link identifications.

Purpose of the Study:

• To introduce a novel filter, the mono- and intralink filter (mi-filter), for enhancing the reliability of XL-MS data.
• To validate the efficacy of the mi-filter across diverse XL-MS datasets and experimental setups.
• To reduce the rate of false-positive inter-protein cross-link identifications in XL-MS analyses.

Main Methods:

• Development of the mono- and intralink filter (mi-filter) logic.
• Application of the mi-filter to various cross-linking mass spectrometry datasets, including protein complexes, affinity enrichments, and cell lysates.
• Comparative analysis of XL-MS data with and without the mi-filter to assess its impact on inter-protein link identification.

Main Results:

• The mi-filter significantly reduces false-positive inter-protein cross-link identifications across all tested XL-MS data types.
• The filter demonstrates broad applicability, working effectively on data from simple complexes to complex proteome-wide samples.
• Implementation of the mi-filter enhances the confidence and reliability of identified PPIs from XL-MS experiments.

Conclusions:

• The mi-filter is an effective and intuitive tool for improving the quality of XL-MS data.
• This filter provides a robust method for increasing confidence in PPIs derived from cross-linking mass spectrometry.
• The mi-filter represents a valuable advancement for systems structural biology research utilizing XL-MS.