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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
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Taxonomic-Level Protein Quantification in Metaproteomics Using a Biomass-Constrained Expectation-Maximization

Gelio Alves1, Mehdi B Hamaneh1, Aleksey Y Ogurtsov1

  • 1Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States.

Journal of the American Society for Mass Spectrometry
|January 15, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced algorithm for metaproteomics, improving the quantification of microbial proteins by addressing the shared peptide problem. The method accurately represents taxonomic-level proteomes in complex microbiome communities.

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Area of Science:

  • Microbiology
  • Proteomics
  • Bioinformatics

Background:

  • Microbiome communities are crucial for ecosystem function and human health.
  • Metaproteomics enables direct identification and quantification of microbial proteins.
  • The shared peptide problem complicates accurate taxon-protein quantification in metaproteomics.

Purpose of the Study:

  • To enhance the Microorganism Classification and Identification (MiCId) workflow by improving taxon-protein quantification.
  • To address the shared peptide problem in mass-spectrometry-based metaproteomics.
  • To enable more accurate representation of taxonomic-level proteomes.

Main Methods:

  • Extended a modified expectation-maximization algorithm with taxonomic biomass constraints.
  • Quantified taxon-protein pairs using clustered identified pairs.
  • Evaluated performance using synthetic and clinical human stool microbiome datasets.

Main Results:

  • Fold changes in simple synthetic datasets closely matched expected values.
  • The algorithm accurately redistributed peptide counts among taxon-protein pairs sharing peptides in a 24-species dataset.
  • MiCId demonstrated accurate and consistent results with previous findings in a clinical stool microbiome dataset.

Conclusions:

  • The enhanced MiCId algorithm robustly quantifies taxon-protein pairs in complex microbial communities.
  • Resolving the shared peptide problem advances the application of metaproteomics in microbiome research.
  • The method enables accurate representation of taxonomic-level proteomes.