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Updated: Jun 7, 2025

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
Published on: August 21, 2019
Discovery and significance of protein-protein interactions in health and disease
Jack F Greenblatt1, Bruce M Alberts2, Nevan J Krogan3
1Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
The identification of individual protein-protein interactions (PPIs) began more than 40 years ago, using protein affinity chromatography and antibody co-immunoprecipitation. As new technologies emerged, analysis of PPIs increased to a genome-wide scale with the introduction of intracellular tagging methods, affinity purification (AP) followed by mass spectrometry (MS), and co-fractionation MS (CF-MS). Now, combining the resulting catalogs of interactions with complementary methods, including crosslinking MS (XL-MS) and cryogenic electron microscopy (cryo-EM), helps distinguish direct interactions from indirect ones within the same or between different protein complexes. These powerful approaches and the promise of artificial intelligence applications like AlphaFold herald a future where PPIs and protein complexes, including energy-driven protein machines, will be understood in exquisite detail, unlocking new insights in the contexts of both basic biology and disease.
The identification of individual protein-protein interactions (PPIs) began more than 40 years ago, using protein affinity chromatography and antibody co-immunoprecipitation. As new technologies emerged, analysis of PPIs increased to a genome-wide scale with the introduction of intracellular tagging methods, affinity purification (AP) followed by mass spectrometry (MS), and co-fractionation MS (CF-MS). Now, combining the resulting catalogs of interactions with complementary methods, including crosslinking MS (XL-MS) and cryogenic electron microscopy (cryo-EM), helps distinguish direct interactions from indirect ones within the same or between different protein complexes. These powerful approaches and the promise of artificial intelligence applications like AlphaFold herald a future where PPIs and protein complexes, including energy-driven protein machines, will be understood in exquisite detail, unlocking new insights in the contexts of both basic biology and disease.
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