The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Identifying specific protein interaction partners using quantitative mass spectrometry and bead proteomes
Open accessAbstract
The identification of interaction partners in protein complexes is a major goal in cell biology. Here we present a reliable affinity purification strategy to identify specific interactors that combines quantitative SILAC-based mass spectrometry with characterization of common contaminants binding to affinity matrices (bead proteomes). This strategy can be applied to affinity purification of either tagged fusion protein complexes or endogenous protein complexes, illustrated here using the well-characterized SMN complex as a model. GFP is used as the tag of choice because it shows minimal nonspecific binding to mammalian cell proteins, can be quantitatively depleted from cell extracts, and allows the integration of biochemical protein interaction data with in vivo measurements using fluorescence microscopy. Proteins binding nonspecifically to the most commonly used affinity matrices were determined using quantitative mass spectrometry, revealing important differences that affect experimental design. These data provide a specificity filter to distinguish specific protein binding partners in both quantitative and nonquantitative pull-down and immunoprecipitation experiments.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Ong, S.-E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A., & Mann, M. (2002) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m200025-mcp200 · Cited by 5,574
Fluorescent Proteins as Proteomic Probes
Cristea, I. M., Williams, R., Chait, B. T., & Rout, M. P. (2005) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m500227-mcp200 · Cited by 251
I-DIRT, A General Method for Distinguishing between Specific and Nonspecific Protein Interactions
Tackett, A. J., DeGrasse, J. A., Sekedat, M. D., Oeffinger, M., Rout, M. P., & Chait, B. T. (2005) · Journal of Proteome Research · doi:10.1021/pr050225e · Cited by 131
Mousson, F., Kolkman, A., Pijnappel, W. W. M. P., Timmers, H. T. M., & Heck, A. J. R. (2008) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m700306-mcp200 · Cited by 67
Cited by
The CRAPome: a contaminant repository for affinity purification–mass spectrometry data
Mellacheruvu, D., Wright, Z., Couzens, A. L., Lambert, J.-P., St-Denis, N. A., Li, T., Miteva, Y. V., Hauri, S., Sardiu, M. E., Low, T. Y., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2557 · Cited by 1,686
A day in the life of the spliceosome
Matera, A. G., & Wang, Z. (2014) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3742 · Cited by 1,074
Mapping intact protein isoforms in discovery mode using top-down proteomics
Tran, J. C., Zamdborg, L., Ahlf, D. R., Lee, J. E., Catherman, A. D., Durbin, K. R., Tipton, J. D., Vellaichamy, A., Kellie, J. F., Li, M., et al. (2011) · Nature · doi:10.1038/nature10575 · Cited by 665
Steinberg, F., Gallon, M., Winfield, M., Thomas, E. C., Bell, A. J., Heesom, K. J., Tavaré, J. M., & Cullen, P. J. (2013) · Nature Cell Biology · doi:10.1038/ncb2721 · Cited by 524
Target Identification for Small Bioactive Molecules: Finding the Needle in the Haystack
Ziegler, S., Pries, V., Hedberg, C., & Waldmann, H. (2013) · Angewandte Chemie International Edition · doi:10.1002/anie.201208749 · Cited by 466
Metadata
- Authors
- Trinkle-Mulcahy, L., Boulon, S., Lam, Y. W., Urcia, R., Boisvert, F.-M., Vandermoere, F., Morrice, N. A., Swift, S., Rothbauer, U., Leonhardt, H., & Lamond, A.
- Venue
- The Journal of Cell Biology
- Publisher
- Rockefeller University Press
- Published
- 2008-10-20
- Volume
- 183
- Issue
- 2
- Pages
- 223-239
- ISSN
- 0021-9525 (ISSN-L)
- 1540-8140
- OpenAlex
- W2015407064
- Cited by
- 446
- References
- 4
- FWCI
- 13.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Identifying specific protein interaction partners using quantitative mass spectrometry and bead proteomes. https://app.cochat.ai/papers/10.1083/jcb.200805092
- The paper
- Trinkle-Mulcahy, L., Boulon, S., Lam, Y. W., Urcia, R., Boisvert, F.-M., Vandermoere, F., Morrice, N. A., Swift, S., Rothbauer, U., Leonhardt, H., & Lamond, A. (2008). Identifying specific protein interaction partners using quantitative mass spectrometry and bead proteomes. The Journal of Cell Biology. https://doi.org/10.1083/jcb.200805092
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
