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Related Concept Videos

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Quantification of Proteins Using Peptide Immunoaffinity Enrichment Coupled with Mass Spectrometry
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BayesENproteomics: Bayesian Elastic Nets for Quantification of Peptidoforms in Complex Samples.

Venkatesh Mallikarjun1,2, Stephen M Richardson2, Joe Swift1,2

  • 1Wellcome Centre for Cell-Matrix Research, University of Manchester, Oxford Road, Manchester M13 9PT, U.K.

Journal of Proteome Research
|April 23, 2020
PubMed
Summary

This study introduces BayesENproteomics, a novel Bayesian elastic net algorithm for quantifying protein and post-translational modification (PTM) levels. It accurately measures protein expression, PTMs, and pathway changes, improving proteomic data analysis.

Keywords:
Bayesian methodsalgorithmsmass spectrometrypathway analysispost-translational modificationproteomics

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Statistical Modeling

Background:

  • Multivariate regression is powerful for quantifying treatment effects and managing noise in mass spectrometry data.
  • Current methods for quantifying endogenous post-translational modifications (PTMs) often overlook protein-level variations.

Purpose of the Study:

  • To compare existing multivariate regression methods with a novel Bayesian elastic net algorithm (BayesENproteomics).
  • To assess the ability of these methods to accurately quantify protein expression and PTMs.
  • To extend the regression approach for pathway-level fold change calculations.

Main Methods:

  • Comparison of three multivariate regression techniques.
  • Development and application of a novel Bayesian elastic net algorithm (BayesENproteomics).
  • Testing on a mixed-species benchmark experiment and synthetic PTMs using stable isotope labeling.

Main Results:

  • BayesENproteomics accurately quantifies protein expression across a wide dynamic range.
  • The method effectively quantifies post-translational modifications (PTMs).
  • Pathway-level fold changes are accurately calculated, complementing enrichment analysis.

Conclusions:

  • BayesENproteomics offers a robust solution for quantifying protein and PTM levels in complex proteomic datasets.
  • The algorithm improves the accuracy of proteomic data analysis by accounting for protein abundance variations.
  • This approach enhances the interpretation of proteomic data by providing pathway-level insights.