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Proteomics

Proteomics

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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Related Experiment Video

Updated: Aug 24, 2025

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

MetaLP: An integrative linear programming method for protein inference in metaproteomics.

Shichao Feng¹, Hong-Long Ji^2,3, Huan Wang⁴

¹Department of Computer Science and Engineering, University of North Texas, Denton, Texas, United States of America.

Plos Computational Biology

|October 21, 2022

Summary

This summary is machine-generated.

Metaproteomics research uses MetaLP, a new method for protein inference. It improves identifying proteins in complex microbial communities by using taxonomic abundance data.

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Last Updated: Aug 24, 2025

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

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A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Published on: January 10, 2025

Area of Science:

Microbiology
Bioinformatics
Proteomics

Background:

Metaproteomics uses tandem mass spectrometry (MS/MS) to characterize microbiome functions.
Protein inference is challenging in metaproteomics due to shared peptides among homologous proteins.

Purpose of the Study:

To develop an improved method for protein inference in metaproteomics.
To address the challenge of distinguishing true from false protein identifications in complex microbial communities.

Main Methods:

Developed MetaLP, an integrative linear programming method for protein inference.
Incorporated taxonomic abundance information from metagenomics or 16S rRNA sequencing as prior information.
Benchmarked MetaLP against existing methods using diverse microbial communities.

Main Results:

MetaLP demonstrated significantly higher protein identification numbers compared to ProteinLP, PeptideProphet, DeepPep, PIPQ, and Sipros Ensemble.
The method showed improved performance across mock, human gut, soil, and marine microbial communities.
Incorporating taxonomic abundance information enhanced the accuracy of protein inference.

Conclusions:

MetaLP substantially improves protein inference for complex metaproteomes.
The integration of taxonomic abundance data within a linear programming framework is key to enhanced protein identification.
MetaLP offers a robust solution for advancing metaproteomic analysis.