A DIA-based quantitative crosslinking mass spectrometryframework for dynamic structural proteomics

Authors

Birklbauer, M. J., Sivakumar Geetha, S., Getreuer, P., Grabmann, G., Hollenstein, D., Kandioller, W., Dorfer, V., Jantsch, V., Mechtler, K., Mueller, F.

Abstract

Proteins undergo dynamic conformational rearrangements and interactions that are central to their biological functions. Quantitative crosslinking mass spectrometry enables the analysis of those dynamics and molecular interactions, but rigorous confidence assessment and empirical validation strategies for quantitative measurements remain underdeveloped, and integrated analysis of complementary structural features, including monolinks and protein-RNA adducts, remains limited. Here we present a data-independent acquisition (DIA)-based framework for quantitative crosslinking mass spectrometry (DIA-QCLMS) that combines optimized acquisition strategies, crosslink-aware spectral libraries and empirical false-discovery-rate (FDR) validation. The workflow supports crosslinks, monolinks and protein-RNA adducts and integrates spectral-library generation from two crosslinking search engines (xiSEARCH and MSAnnika). To enable robust confidence assessment in DIA data, we developed a four-state target-decoy spectral library strategy that explicitly models target-target, target-decoy, decoy-target and decoy-decoy crosslink spectra. Experimental entrapment datasets enabled empirical validation of confidence estimation, whereas benchmarking with PhoX-crosslinked Cas9 demonstrated improved quantitative completeness and reproducibility compared with data-dependent acquisition. Application of the workflow to the ATP-dependent RNA helicase UAP56 (DDX39B) resolved ligand-dependent changes in intramolecular restraints, residue accessibility and candidate RNA-contact sites associated with the transition from an open to a clamped conformation. These results establish DIA-QCLMS as a scalable framework for quantitative structural proteomics and provide practical strategies for confidence-controlled analysis of dynamic protein interactions and conformational states.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 18 Jun 2026.

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