Limits...
Identifying specific protein interaction partners using quantitative mass spectrometry and bead proteomes.

Trinkle-Mulcahy L, Boulon S, Lam YW, Urcia R, Boisvert FM, Vandermoere F, Morrice NA, Swift S, Rothbauer U, Leonhardt H, Lamond A - J. Cell Biol. (2008)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Gene Regulation and Expression, University of Dundee, Dundee, Scotland, UK. ltrinkle@uottawa.ca

ABSTRACT
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.

Show MeSH
Systematic analysis of SILAC datasets. Quantitative mass spectrometric data generated by the cytoplasmic and nuclear GFP-SMN immunoprecipitation experiments were subjected to a standard analysis workflow. First, the frequency of specific SILAC (heavy/light amino acid) ratios were plotted for the entire datasets to determine the distribution of these ratios among the proteins identified (A). Environmental contaminants such as keratins have very low ratios and cluster near 0. In the cytoplasmic experiment, proteins that bind nonspecifically to the bead matrix cluster in a bell curve distribution around 1, as expected for proteins that bind equally in the light and heavy form. The threshold for detection of bona fide interaction partners was set at a conservative level above that (hashed red line). Note that in the nuclear experiment the SILAC ratios for the bead contaminants were shifted to the left, clustering in a bell curve distribution around the higher value of 1.5. In this case the threshold (hashed blue line) must also be shifted. SMN itself, all of the core SMN complex members, and several known interacting partners fell above this threshold and were identified in this first analysis step. However, less abundant or lower affinity binding partners may be found at or below these conservative threshold values. Analysis of the datasets is thus further extended by applying the Sepharose bead proteome as a filter and grouping the SILAC ratios of those proteins that have been identified as binding nonspecifically to this bead matrix, as shown here for the cytoplasmic dataset (B). Most proteins known to bind Sepharose (gray) and potential GFP-binding proteins (green) have the expected ratios near or below threshold, but a few are significantly above threshold and must be considered as potentially real interacting proteins, albeit with a lower priority for further analysis. SILAC ratios calculated for the remaining proteins in the dataset, i.e., those not known to bind nonspecifically to either the GFP tag or the bead matrix, are next plotted separately (C). Over two-thirds of the proteins have SILAC ratios significantly higher than threshold. These include both known and novel interacting partners for SMN. Some of the known SMN complex interacting partners, such as PRMT5 and Unrip, have ratios closer to threshold, and thus would be overlooked in a threshold-based analysis. As expected for such a well-characterized complex, very few novel proteins were detected. One of these, USP9X, was selected for further analysis.
© Copyright Policy
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC2568020&req=5

fig5: Systematic analysis of SILAC datasets. Quantitative mass spectrometric data generated by the cytoplasmic and nuclear GFP-SMN immunoprecipitation experiments were subjected to a standard analysis workflow. First, the frequency of specific SILAC (heavy/light amino acid) ratios were plotted for the entire datasets to determine the distribution of these ratios among the proteins identified (A). Environmental contaminants such as keratins have very low ratios and cluster near 0. In the cytoplasmic experiment, proteins that bind nonspecifically to the bead matrix cluster in a bell curve distribution around 1, as expected for proteins that bind equally in the light and heavy form. The threshold for detection of bona fide interaction partners was set at a conservative level above that (hashed red line). Note that in the nuclear experiment the SILAC ratios for the bead contaminants were shifted to the left, clustering in a bell curve distribution around the higher value of 1.5. In this case the threshold (hashed blue line) must also be shifted. SMN itself, all of the core SMN complex members, and several known interacting partners fell above this threshold and were identified in this first analysis step. However, less abundant or lower affinity binding partners may be found at or below these conservative threshold values. Analysis of the datasets is thus further extended by applying the Sepharose bead proteome as a filter and grouping the SILAC ratios of those proteins that have been identified as binding nonspecifically to this bead matrix, as shown here for the cytoplasmic dataset (B). Most proteins known to bind Sepharose (gray) and potential GFP-binding proteins (green) have the expected ratios near or below threshold, but a few are significantly above threshold and must be considered as potentially real interacting proteins, albeit with a lower priority for further analysis. SILAC ratios calculated for the remaining proteins in the dataset, i.e., those not known to bind nonspecifically to either the GFP tag or the bead matrix, are next plotted separately (C). Over two-thirds of the proteins have SILAC ratios significantly higher than threshold. These include both known and novel interacting partners for SMN. Some of the known SMN complex interacting partners, such as PRMT5 and Unrip, have ratios closer to threshold, and thus would be overlooked in a threshold-based analysis. As expected for such a well-characterized complex, very few novel proteins were detected. One of these, USP9X, was selected for further analysis.

Mentions: We have developed a useful strategy for analyzing the SILAC data to help distinguish specific interactions (Fig. 5). Data acquired from SILAC-based quantitative immunoprecipitation experiments are first plotted in a histogram. This helps to visualize the grouping of nonspecific binding proteins, which generally fall within a bell-shaped curve regardless of the absolute value of the SILAC ratios. Although under ideal conditions a ratio of 1 should be obtained for nonspecific binding, this absolute value can vary experimentally in either direction. This is illustrated in Fig. 5 A, where the absolute peak values for the bell-shaped curves for the separate nuclear and cytoplasmic extracts differ slightly. Within each experiment, the SILAC ratios can thus be evaluated with respect to the actual background ratio curve determined and a corresponding threshold set for that experiment (Fig. 5 A, hashed blue and red lines).


Identifying specific protein interaction partners using quantitative mass spectrometry and bead proteomes.

Trinkle-Mulcahy L, Boulon S, Lam YW, Urcia R, Boisvert FM, Vandermoere F, Morrice NA, Swift S, Rothbauer U, Leonhardt H, Lamond A - J. Cell Biol. (2008)

Systematic analysis of SILAC datasets. Quantitative mass spectrometric data generated by the cytoplasmic and nuclear GFP-SMN immunoprecipitation experiments were subjected to a standard analysis workflow. First, the frequency of specific SILAC (heavy/light amino acid) ratios were plotted for the entire datasets to determine the distribution of these ratios among the proteins identified (A). Environmental contaminants such as keratins have very low ratios and cluster near 0. In the cytoplasmic experiment, proteins that bind nonspecifically to the bead matrix cluster in a bell curve distribution around 1, as expected for proteins that bind equally in the light and heavy form. The threshold for detection of bona fide interaction partners was set at a conservative level above that (hashed red line). Note that in the nuclear experiment the SILAC ratios for the bead contaminants were shifted to the left, clustering in a bell curve distribution around the higher value of 1.5. In this case the threshold (hashed blue line) must also be shifted. SMN itself, all of the core SMN complex members, and several known interacting partners fell above this threshold and were identified in this first analysis step. However, less abundant or lower affinity binding partners may be found at or below these conservative threshold values. Analysis of the datasets is thus further extended by applying the Sepharose bead proteome as a filter and grouping the SILAC ratios of those proteins that have been identified as binding nonspecifically to this bead matrix, as shown here for the cytoplasmic dataset (B). Most proteins known to bind Sepharose (gray) and potential GFP-binding proteins (green) have the expected ratios near or below threshold, but a few are significantly above threshold and must be considered as potentially real interacting proteins, albeit with a lower priority for further analysis. SILAC ratios calculated for the remaining proteins in the dataset, i.e., those not known to bind nonspecifically to either the GFP tag or the bead matrix, are next plotted separately (C). Over two-thirds of the proteins have SILAC ratios significantly higher than threshold. These include both known and novel interacting partners for SMN. Some of the known SMN complex interacting partners, such as PRMT5 and Unrip, have ratios closer to threshold, and thus would be overlooked in a threshold-based analysis. As expected for such a well-characterized complex, very few novel proteins were detected. One of these, USP9X, was selected for further analysis.
© Copyright Policy
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC2568020&req=5

fig5: Systematic analysis of SILAC datasets. Quantitative mass spectrometric data generated by the cytoplasmic and nuclear GFP-SMN immunoprecipitation experiments were subjected to a standard analysis workflow. First, the frequency of specific SILAC (heavy/light amino acid) ratios were plotted for the entire datasets to determine the distribution of these ratios among the proteins identified (A). Environmental contaminants such as keratins have very low ratios and cluster near 0. In the cytoplasmic experiment, proteins that bind nonspecifically to the bead matrix cluster in a bell curve distribution around 1, as expected for proteins that bind equally in the light and heavy form. The threshold for detection of bona fide interaction partners was set at a conservative level above that (hashed red line). Note that in the nuclear experiment the SILAC ratios for the bead contaminants were shifted to the left, clustering in a bell curve distribution around the higher value of 1.5. In this case the threshold (hashed blue line) must also be shifted. SMN itself, all of the core SMN complex members, and several known interacting partners fell above this threshold and were identified in this first analysis step. However, less abundant or lower affinity binding partners may be found at or below these conservative threshold values. Analysis of the datasets is thus further extended by applying the Sepharose bead proteome as a filter and grouping the SILAC ratios of those proteins that have been identified as binding nonspecifically to this bead matrix, as shown here for the cytoplasmic dataset (B). Most proteins known to bind Sepharose (gray) and potential GFP-binding proteins (green) have the expected ratios near or below threshold, but a few are significantly above threshold and must be considered as potentially real interacting proteins, albeit with a lower priority for further analysis. SILAC ratios calculated for the remaining proteins in the dataset, i.e., those not known to bind nonspecifically to either the GFP tag or the bead matrix, are next plotted separately (C). Over two-thirds of the proteins have SILAC ratios significantly higher than threshold. These include both known and novel interacting partners for SMN. Some of the known SMN complex interacting partners, such as PRMT5 and Unrip, have ratios closer to threshold, and thus would be overlooked in a threshold-based analysis. As expected for such a well-characterized complex, very few novel proteins were detected. One of these, USP9X, was selected for further analysis.
Mentions: We have developed a useful strategy for analyzing the SILAC data to help distinguish specific interactions (Fig. 5). Data acquired from SILAC-based quantitative immunoprecipitation experiments are first plotted in a histogram. This helps to visualize the grouping of nonspecific binding proteins, which generally fall within a bell-shaped curve regardless of the absolute value of the SILAC ratios. Although under ideal conditions a ratio of 1 should be obtained for nonspecific binding, this absolute value can vary experimentally in either direction. This is illustrated in Fig. 5 A, where the absolute peak values for the bell-shaped curves for the separate nuclear and cytoplasmic extracts differ slightly. Within each experiment, the SILAC ratios can thus be evaluated with respect to the actual background ratio curve determined and a corresponding threshold set for that experiment (Fig. 5 A, hashed blue and red lines).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Gene Regulation and Expression, University of Dundee, Dundee, Scotland, UK. ltrinkle@uottawa.ca

ABSTRACT
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.

Show MeSH