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Predicting Electrophoretic Mobility of Proteoforms for Large-Scale Top-Down Proteomics.

Daoyang Chen1, Rachele A Lubeckyj1, Zhichang Yang1

  • 1Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States.

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|February 12, 2020
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This study introduces a novel method to predict proteoform separation times in capillary zone electrophoresis-tandem mass spectrometry (CZE-MS/MS). A validated semiempirical model accurately predicts electrophoretic mobility, enhancing proteoform identification confidence.

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biochemistry

Background:

  • Top-down proteomics uses CZE-MS/MS for high-throughput proteoform characterization.
  • False discovery rate (FDR) is standard for filtering proteoform identifications but can be underestimated.
  • Accurate prediction of proteoform separation times offers an alternative confidence metric.

Purpose of the Study:

  • To evaluate semiempirical models for predicting proteoform electrophoretic mobility (μef) in CZE-MS/MS.
  • To establish a reliable method for assessing proteoform identification confidence beyond FDR.
  • To investigate factors influencing proteoform electrophoretic mobility.

Main Methods:

  • Utilized large-scale top-down proteomics data from CZE-MS/MS.
  • Developed and tested various semiempirical models to predict proteoform electrophoretic mobility.
  • Correlated predicted μef with experimentally observed values for *E. coli* proteoforms.

Main Results:

  • Achieved a high linear correlation (R² = 0.98) between predicted and experimental μef using a simple semiempirical model.
  • The model effectively uses proteoform charge and molecular mass.
  • Complete proteoform unfolding in CZE enhances prediction accuracy.
  • N-terminal acetylation and phosphorylation were found to reduce proteoform charge by approximately one unit.

Conclusions:

  • A validated semiempirical model accurately predicts proteoform electrophoretic mobility in CZE-MS/MS.
  • This predictive approach offers a valuable complementary method for validating proteoform identifications.
  • Understanding charge modifications like N-terminal acetylation and phosphorylation improves proteoform analysis.