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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 proteomics...

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

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Related Experiment Video

Updated: May 19, 2026

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

Bioinformatic challenges in targeted proteomics.

Daniel Reker¹, Lars Malmström

¹ETH Zurich, Wolfgang-Pauli-Strasse 16, 8093 Zurich, Switzerland.

Journal of Proteome Research

|August 8, 2012

Summary

This summary is machine-generated.

Selected reaction monitoring mass spectrometry requires extensive sample knowledge for parameter setting. This review explores computational methods for de novo parameter estimation, addressing bioinformatical challenges in targeted proteomics.

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Area of Science:

Proteomics
Mass Spectrometry
Bioinformatics

Background:

Selected reaction monitoring mass spectrometry (SRM-MS) is a sensitive targeted proteomics technique.
Accurate experimental parameter setting is crucial but often relies on prior data.
De novo parameter estimation is necessary when prior data is unavailable.

Purpose of the Study:

To provide an overview of bioinformatical challenges in de novo parameter estimation for SRM-MS.
To frame these challenges using classical machine learning and data mining concepts.
To encourage the development of computational methods for targeted proteomics.

Main Methods:

Reviewing existing literature and computational approaches.
Framing bioinformatical problems in machine learning and data mining terms.
Providing examples of implemented solutions and suggesting alternatives.

Main Results:

Identified key bioinformatical challenges in automated SRM-MS parameter estimation.
Highlighted the limited availability of current computational applications.
Demonstrated the potential of machine learning for addressing these challenges.

Conclusions:

Computational methods are essential for de novo parameter estimation in SRM-MS.
Further development of algorithms and assisted workflows is needed.
Integrating computational approaches will advance targeted proteomics research and application.