ArticleMolecular & cellular proteomics : MCP2025
Analysis of Limited Proteolysis-Coupled Mass Spectrometry Data.
Article in Molecular & cellular proteomics : MCP, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- ProteoMeter: a pipeline for integrating multi-PTM and limited proteolysis data to reveal modification-structure coupling at the residue level.NAR genomics and bioinformatics · 2026Article
- Limited Proteolysis Mass Spectrometry to Identify Protein Structural Differences in Brain Tissue.Bio-protocol · 2026Article
- Detecting misfolded non-covalent lasso entanglements in protein structures, simulation trajectories, and mass spectrometry data.bioRxiv : the preprint server for biology · 2026Article
- 3D Proteomics: Structural, Functional, Chemical and Biomarker Discovery Proteomics With LiP-MS.Molecular & cellular proteomics : MCP · 2026Review
- Advancing DIA-Based Limited Proteolysis Workflows: Introducing DIA-LiPA.Analytical chemistry · 2026Article
- Mass Spectrometry Proteomics: A Key to Faster Drug Discovery.Journal of medicinal chemistry · 2026Review
- Chaperone dependency during biogenesis does not correlate with chaperone dependency during refolding.Molecular systems biology · 2026Article
- A shrinkage-based statistical method for testing group mean differences in quantitative bottom-up proteomics.BMC bioinformatics · 2025Article
- Chaperone Dependency during Primary Protein Biogenesis Does Not Correlate with Chaperone Dependency duringbioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Limited proteolysis combined with mass spectrometry (LiP-MS) facilitates probing structural changes on a proteome-wide scale. This method leverages differences in the proteinase K accessibility of native protein structures to concurrently assess structural alterations for thousands of proteins in situ. Distinguishing different contributions to the LiP-MS signal, such as changes in protein abundance or chemical modifications, from structural protein alterations remains challenging. Here, we present the first comprehensive computational pipeline to infer structural alterations for LiP-MS data using a two-step approach. 1) We remove unwanted variations from the LiP signal that are not caused by protein structural effects and 2) infer the effects of variables of interest on the remaining signal. Using LiP-MS data from three species, we demonstrate that this approach outperforms previously employed approaches. Our framework provides a uniquely powerful approach for deconvolving LiP-MS signals and separating protein structural changes from changes in protein abundance, posttranslational modifications, and alternative splicing. Our approach may also be applied to analyze other types of peptide-centric structural proteomics data, such as FPOP or molecular painting data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.