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Data-independent acquisition (DIA) approach for comprehensive ubiquitinome profiling in targeted protein degradation

  • Gajanan Sathe
  • , Sascha Roth
  • , Géraldine Gelders
  • , Abigail Brewer
  • , Thomas J. MacArtney
  • , Nicola Wood
  • , Louis De Muynck
  • , Mark Nakasone
  • , Toan Phung
  • , Arjan Buist
  • , Diederik W.E. Moechars
  • , Gopal Sapkota (Lead / Corresponding author)

Research output: Contribution to journal › Article › peer-review

Abstract

Targeted protein degradation (TPD) has emerged as a highly promising therapeutic strategy for a wide range of diseases, including cancer and neurodegenerative disorders. The ubiquitinproteasome system, which is responsible for protein degradation, plays a critical role in this process. Gaining comprehensive insights into the ubiquitylation landscape is essential for the development of selective and efficient targeted protein degradation approaches. Recently, data-independent acquisition (DIA) has gained significant popularity as a robust and unbiased approach for quantitative proteomics. Here, we report a robust workflow that utilizes diGly antibody-based enrichment followed by an optimized Orbitrap-based DIA method for the identification of ubiquitylated peptides. We identify over 40,000 diGly peptides corresponding to more than 7,000 proteins in a single measurement from cells exposed to a proteasome inhibitor, highlighting an exceptional throughput. By applying our optimized workflow, we successfully identify ubiquitylation sites on substrate proteins with various TPD approaches. By also leveraging the cutting-edge Orbitrap Astral technology, we resolve rapid PROTACdriven ubiquitylation on target proteins, as early as 5 minutes post-treatment. Therefore, our workflow holds tremendous potential for rapidly establishing mode-of-action for various TPD modalities, including PROTACs and molecular glues.
Original languageEnglish
JournalScientific Reports
Early online date15 Sept 2026
DOIs
Publication statusE-pub ahead of print - 15 Sept 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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