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Area of Science:

  • Proteomics
  • Biochemistry
  • Mass Spectrometry

Background:

  • Cross-linking mass spectrometry (XL-MS) investigates protein-protein interactions (PPI) in complex samples.
  • Tandem mass tag (TMT) labeling enables large-scale PPI quantification via XL-MS.
  • Existing TMT-based XL-MS methods lack benchmarking datasets and parameter optimization.

Purpose of the Study:

  • Establish a robust TMT-based XL-MS quantification method.
  • Generate a benchmarking dataset for evaluating XL-MS parameters.
  • Optimize MS acquisition strategies for accurate PPI quantification.

Main Methods:

  • Created a two-interactome dataset using TMT-labeled cross-linked E. coli and HEK293T lysates.
  • Assessed cross-link identification using MS2- and MS3-based XL-MS methods.
  • Optimized stepped higher energy collisional dissociation (HCD) energies for TMT-labeled cross-links.
  • Evaluated quantification accuracy and dispersion of MS2-, MS3-, and SPS-MS3 methods.

Main Results:

  • Identified a need for higher fragmentation energies for TMT-labeled cross-links compared to unlabeled ones.
  • A stepped HCD-MS2 method with energies 36-42-48 yielded numerous quantifiable cross-links.
  • Achieved high quantification accuracy with the optimized stepped HCD-MS2 method.

Conclusions:

  • The developed stepped HCD-MS2 method offers a robust and widely applicable approach for quantitative PPI characterization.
  • This method facilitates multiplexed PPI analysis in complex biological systems.
  • The established benchmarking dataset aids future XL-MS method development and validation.