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Attribute-Weighted Aggregation of Tandem Mass Reporter Ion Intensity for Protein Quantification Using Isobaric

Jiahua Tan1, Gian L Negri2, Gregg B Morin2,3

  • 1Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada.

Journal of Proteome Research
|September 23, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an attribute-weighted aggregation (AWA) method for more accurate protein abundance estimation in proteomics. AWA improves quantitative accuracy by weighting peptide-spectrum matches (PSMs) based on their attributes, enhancing differential expression analysis recall and precision.

Keywords:
Isobaric labelingPSM aggregationProtein quantification

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Area of Science:

  • Proteomics
  • Quantitative Biology
  • Bioinformatics

Background:

  • Isobaric labeling is a standard proteomics technique for protein quantification.
  • Bottom-up proteomics aggregates peptide-spectrum matches (PSMs) for protein abundance estimation.
  • Current aggregation methods assume equal quantitative accuracy for all PSMs, which is often not true due to variations in ionizability and interference.

Purpose of the Study:

  • To develop a novel method, attribute-weighted aggregation (AWA), for more accurate protein abundance estimation.
  • To improve the quantitative accuracy of PSMs by considering their intrinsic attributes.
  • To enhance the performance of differential expression analysis in proteomics studies.

Main Methods:

  • Developed an attribute-weighted aggregation (AWA) method incorporating PSM attributes and reporter ion intensities.
  • Trained a random forest model on PSM characteristics using three spike-in datasets to predict quantitative inaccuracy.
  • Aggregated PSMs to the protein level based on predicted inaccuracy, weighting them accordingly.

Main Results:

  • AWA demonstrated improved recall in differential expression analyses across multiple datasets while maintaining high precision.
  • The method was successfully applied to two large cancer cohorts, validating its utility.
  • An R package, AWAggregator, was developed for easy implementation and model retraining.

Conclusions:

  • Attribute-weighted aggregation (AWA) offers a more accurate approach to protein abundance estimation in proteomics.
  • AWA enhances the reliability of quantitative proteomics data, particularly in differential expression studies.
  • The AWAggregator R package facilitates the adoption and application of this improved quantitative method.