The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Affinity purification–mass spectrometry and network analysis to understand protein-protein interactions
Open accessAbstract
By determining protein-protein interactions in normal, diseased and infected cells, we can improve our understanding of cellular systems and their reaction to various perturbations. In this protocol, we discuss how to use data obtained in affinity purification–mass spectrometry (AP-MS) experiments to generate meaningful interaction networks and effective figures. We begin with an overview of common epitope tagging, expression and AP practices, followed by liquid chromatography–MS (LC-MS) data collection. We then provide a detailed procedure covering a pipeline approach to (i) pre-processing the data by filtering against contaminant lists such as the Contaminant Repository for Affinity Purification (CRAPome) and normalization using the spectral index (SIN) or normalized spectral abundance factor (NSAF); (ii) scoring via methods such as MiST, SAInt and CompPASS; and (iii) testing the resulting scores. Data formats familiar to MS practitioners are then transformed to those most useful for network-based analyses. The protocol also explores methods available in Cytoscape to visualize and analyze these types of interaction data. The scoring pipeline can take anywhere from 1 d to 1 week, depending on one's familiarity with the tools and data peculiarities. Similarly, the network analysis and visualization protocol in Cytoscape takes 2–4 h to complete with the provided sample data, but we recommend taking days or even weeks to explore one's data and find the right questions.
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SAINT: probabilistic scoring of affinity purification–mass spectrometry data
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Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae
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Global landscape of HIV–human protein complexes
Jäger, S., Cimermancic, P., Gulbahce, N., Johnson, J. R., McGovern, K. E., Clarke, S. C., Shales, M., Mercenne, G., Pache, L., Li, K., et al. (2012) · Nature · doi:10.1038/nature10719 · Cited by 731
Self-citationGuilt-by-association goes global
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Systematic interpretation of genetic interactions using protein networks
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Cited by
Mohammed, H., Taylor, C., Brown, G. D., Papachristou, E. K., Carroll, J. S., & D'Santos, C. S. (2016) · Nature Protocols · doi:10.1038/nprot.2016.020 · Cited by 353
Learning from Co-expression Networks: Possibilities and Challenges
Serin, E. A. R., Nijveen, H., Hilhorst, H. W. M., & Ligterink, W. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.00444 · Cited by 304
Yugandhar, K., Gupta, S., & Yu, H. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.05.007 · Cited by 51
“This might include non-specific binding of proteins to beads, resins or might result from biological factors such as protein misfolding [94].”
BackgroundInfluentialInactivation of PP2A by a recurrent mutation drives resistance to MEK inhibitors
O’Connor, C. M., Leonard, D., Wiredja, D., Avelar, R. A., Wang, Z., Schlatzer, D., Bryson, B., Tokala, E., Taylor, S. E., Upadhyay, A., et al. (2020) · Oncogene · doi:10.1038/s41388-019-1012-2 · Cited by 48
Systematic identification of structure-specific protein–protein interactions
(2024) · doi:10.1038/s44320-024-00037-6 · Cited by 34
Metadata
- Authors
- Morris, J. H., Knudsen, G. M., Verschueren, E., Johnson, J. R., Cimermancic, P., Greninger, A. L., & Pico, A. R.
- Venue
- Nature Protocols
- Published
- 2014-10-02
- Volume
- 9
- Issue
- 11
- Pages
- 2539-2554
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W1990640571
- Cited by
- 198
- References
- 18
- FWCI
- 6.26
- Type
- journal article
- Language
- en
- Open access
- green
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