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Software-Assisted Data Processing Workflow for Intact Glycoprotein Mass Spectrometry.

Alan B Moran1, Elena Domínguez-Vega1, Manfred Wuhrer1

  • 1Center for Proteomics and Metabolomics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands.

Journal of Proteome Research
|March 1, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a streamlined workflow for intact protein analysis using mass spectrometry and deconvolution. The software-assisted approach enables faster and more efficient quantification of proteoforms, crucial for clinical applications.

Keywords:
data processingdeconvolutionintact proteinmass spectrometryprostate-specific antigenproteoforms

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Biochemistry

Background:

  • Intact protein analysis is vital for identifying proteoforms missed by other methods.
  • Current quantification methods (extracted ion, deconvolution) lack a standardized best practice.
  • Existing tools are often inefficient, hindering clinical cohort analysis.

Purpose of the Study:

  • To develop and assess a software-assisted workflow for streamlined intact protein data processing.
  • To improve integration, annotation, and quantification via deconvolution.
  • To enable cross-validation of proteoform assignments using orthogonal data sets.

Main Methods:

  • Implemented a software-assisted workflow for intact protein analysis.
  • Utilized deconvolution for data quantification.
  • Employed middle-up and bottom-up mass spectrometry for cross-validation.
  • Analyzed seminal prostate-specific antigen (PSA) and PSA from patient urine samples.

Main Results:

  • The workflow efficiently processes intact protein data, including integration, annotation, and quantification.
  • Cross-validation confirmed cleavage proteoform assignments for PSA.
  • Deconvolution quantification of urinary PSA correlated well with manual methods.

Conclusions:

  • The presented workflow offers a fast and efficient solution for intact protein data processing.
  • This approach enhances the throughput necessary for analyzing clinical cohorts.
  • The study provides a valuable tool for advancing proteomic research and clinical diagnostics.