Journal
PROTEOMICS
Volume 11, Issue 6, Pages 1153-1159Publisher
WILEY
DOI: 10.1002/pmic.201000548
Keywords
Cell biology; Cumulative analysis; Immuno-precipitation; Protein frequency; Quality control; SILAC
Funding
- Wellcome Trust [073980/Z/03/Z]
- European Union (EU)
- EU Network of Excellence Grant European Alternative Splicing Network (EURASNET)
- Radical Solutions for Researching the Proteome (RASOR) initiative
- Biotechnology and Biological Sciences Research Council (BBSRC)
- Engineering and Physical Sciences Research Council
- Scottish Higher Education Funding Council
- Medical Research Council (MRC)
- Medical Research Council [G0301131, G0801738] Funding Source: researchfish
- MRC [G0801738, G0301131] Funding Source: UKRI
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Immuno-precipitation (IP) experiments using MS provide a sensitive and accurate way of characterising protein complexes and their response to regulatory mechanisms. Differences in stoichiometry can be determined as well as the reliable identification of specific binding partners. The quality control of IP and protein interaction studies has its basis in the biology that is being observed. Is that unusual protein identification a genuine novelty, or an experimental irregularity? Antibodies and the solid matrices used in these techniques isolate not only the target protein and its specific interaction partners but also many non-specific 'contaminants' requiring a structured analysis strategy. These methodological developments and the speed and accuracy of MS machines, which has been increasing consistently in the last 5 years, have expanded the number of proteins identified and complexity of analysis. The European Science Foundation's Frontiers in Functional Genomics programme 'Quality Control in Proteomics' Workshop provided a forum for disseminating knowledge and experience on this subject. Our aim in this technical brief is to outline clearly, for the scientists wanting to carry out this kind of experiment, and recommend what, in our experience, are the best potential ways to design an IP experiment, to help identify possible pitfalls, discuss important controls and outline how to manage and analyse the large amount of data generated. Detailed experimental methodologies have been referenced but not described in the form of protocols.
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