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Related Experiment Video

Updated: Jun 23, 2026

Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics
12:53

Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

Published on: July 6, 2014

A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics.

Jürgen Cox1, Ivan Matic, Maximiliane Hilger

  • 1Department for Proteomics and Signal Transduction, Max-Planck Institute for Biochemistry, Martinsried, Germany. cox@biochem.mpg.de

Nature Protocols
|April 18, 2009
PubMed
Summary
This summary is machine-generated.

MaxQuant software enables automated, reliable quantification of thousands of proteins from mass spectrometry data. This free tool supports complex experimental designs, including stable isotope labeling by amino acids in cell culture (SILAC), on standard computers.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Quantitative proteomics generates large mass spectrometry datasets.
  • Analyzing these datasets requires specialized software.
  • High-resolution mass spectrometry (MS) is crucial for accurate quantification.

Purpose of the Study:

  • To provide a step-by-step protocol for using MaxQuant software.
  • To demonstrate MaxQuant's capabilities for analyzing stable isotope labeling by amino acids in cell culture (SILAC) data.
  • To highlight MaxQuant's suitability for complex experimental designs and large-scale data analysis.

Main Methods:

  • Utilizing MaxQuant software for quantitative proteomics.
  • Processing high-resolution mass spectrometry data from Thermo LTQ-Orbitrap and LTQ-FT-ICR instruments.
  • Applying MaxQuant to stable isotope labeling by amino acids in cell culture (SILAC) experiments with double or triple labeling.
  • Leveraging Mascot as the search engine within the MaxQuant workflow.

Main Results:

  • MaxQuant automatically and reliably quantifies hundreds of thousands of peptides and thousands of proteins in SILAC experiments.
  • The software supports complex experimental designs, including time series and drug-response data.
  • Computational requirements are met by standard desktop computers, even for large projects (>1,000 LC/MS runs).
  • Output tables provide comprehensive protein information (Gene Ontology, domains, pathways) for further analysis.

Conclusions:

  • MaxQuant is an efficient, user-friendly software package for quantitative proteomics.
  • It is well-suited for analyzing large-scale SILAC data and complex experimental designs.
  • The software provides valuable, ready-to-use bioinformatics data, enhancing downstream analysis.