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Peptide and Protein Quantification Using Automated Immuno-MALDI iMALDI
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Peptide filtering differently affects the performances of XIC-based quantification methods.

Isma Belouah1, Mélisande Blein-Nicolas2, Thierry Balliau2

  • 1UMR 1332 BFP, INRA, Univ Bordeaux, F33883 Villenave d'Ornon, France.

Journal of Proteomics
|October 13, 2018
PubMed
Summary

This study investigates how filtering peptide data impacts protein quantification accuracy in bottom-up proteomics. Results show that optimal peptide filtering strategies significantly improve quantification method performance, enhancing overall protein abundance estimation.

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biochemistry

Background:

  • Bottom-up proteomics relies on peptide data for protein abundance estimation.
  • Current quantification methods often treat peptide data filtering and quantification strategies independently.
  • Understanding the interplay between filtering and quantification is crucial for accurate protein abundance determination.

Purpose of the Study:

  • To investigate the impact of various peptide data filters on the performance of different XIC-based quantification methods.
  • To determine how the choice of peptide filter affects protein abundance estimation accuracy.
  • To identify optimal strategies for peptide data processing and quantification in bottom-up proteomics.

Main Methods:

  • A spike-in experiment using Universal Protein Standard was conducted.
  • Four distinct peptide filters were applied to create five datasets.
  • Five XIC-based quantification methods were evaluated across these datasets.
  • Protein quantification quality was assessed based on precision, accuracy, and linearity.

Main Results:

  • The effectiveness of peptide filters varied depending on the quantification method and data type.
  • Filters had contrasting effects based on the specific quantification objective (absolute vs. relative).
  • Intensity modeling demonstrated the highest robustness, performing best without filters.
  • Appropriate peptide filtering enabled comparable performances across different quantification methods.

Conclusions:

  • Peptide filtering and quantification method selection are interdependent for accurate protein quantification.
  • The choice of filters should align with the experimental design and quantification goals.
  • Intensity modeling offers a robust approach, but optimized filtering can enhance other methods.
  • This research provides critical insights for optimizing bottom-up proteomics workflows.