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Establishment of a protein frequency library and its application in the reliable identification of specific protein interaction partners.

Boulon S, Ahmad Y, Trinkle-Mulcahy L, Verheggen C, Cobley A, Gregor P, Bertrand E, Whitehorn M, Lamond AI - Mol. Cell Proteomics (2009)

Bottom Line: The PFL is a dynamic tool that can be filtered for specific experimental parameters to generate a customized library.It will be continuously updated as data from each new experiment are added to the library, thereby progressively enhancing its utility.The application of the PFL to pulldown experiments is especially helpful in identifying either lower abundance or less tightly bound specific components of protein complexes that are otherwise lost among the large, nonspecific background.

View Article: PubMed Central - PubMed

Affiliation: The Wellcome Trust Centre for Gene Regulation and Expression, University of Dundee, Dundee DD1 5EH, Scotland, United Kingdom.

ABSTRACT
The reliable identification of protein interaction partners and how such interactions change in response to physiological or pathological perturbations is a key goal in most areas of cell biology. Stable isotope labeling with amino acids in cell culture (SILAC)-based mass spectrometry has been shown to provide a powerful strategy for characterizing protein complexes and identifying specific interactions. Here, we show how SILAC can be combined with computational methods drawn from the business intelligence field for multidimensional data analysis to improve the discrimination between specific and nonspecific protein associations and to analyze dynamic protein complexes. A strategy is shown for developing a protein frequency library (PFL) that improves on previous use of static "bead proteomes." The PFL annotates the frequency of detection in co-immunoprecipitation and pulldown experiments for all proteins in the human proteome. It can provide a flexible and objective filter for discriminating between contaminants and specifically bound proteins and can be used to normalize data values and facilitate comparisons between data obtained in separate experiments. The PFL is a dynamic tool that can be filtered for specific experimental parameters to generate a customized library. It will be continuously updated as data from each new experiment are added to the library, thereby progressively enhancing its utility. The application of the PFL to pulldown experiments is especially helpful in identifying either lower abundance or less tightly bound specific components of protein complexes that are otherwise lost among the large, nonspecific background.

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Visualization of contaminant profiles and threshold levels. A representative example of a triple SILAC co-IP experiment using GFP-Pol2C as bait in cells either with or without α-amanitin treatment2 was used to generate the graphs shown. A, graphs showing median SILAC ratios for every protein group identified and quantified by MaxQuant (604 distinct protein groups) with each protein group plotted on the x axis and the median SILAC value for that protein group plotted on the y axis. Two arbitrarily chosen thresholds are illustrated (black horizontal lines in left and right panels). B, representative ratio distribution plots. Data are plotted as a histogram with log2 SILAC ratios on the x axis and number of proteins for a given ratio on the y axis. Nonspecific contaminants reproducibly cluster in a Gaussian (normal) distribution centered at ∼0 (left panel), although the exact mean can deviate from 0 due to experimental variability as seen for the GFP-Pol2C data set (right panel). C, data from the GFP-Pol2C data set plotted with log2(M/L) SILAC ratio on the x axis and log2(H/M) SILAC ratio on the y axis with each point corresponding to the ratio value for a specific protein group. The bait protein is shown in red. Putative experimental contaminants (Experim. contamin.) cluster around the origin.
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Figure 2: Visualization of contaminant profiles and threshold levels. A representative example of a triple SILAC co-IP experiment using GFP-Pol2C as bait in cells either with or without α-amanitin treatment2 was used to generate the graphs shown. A, graphs showing median SILAC ratios for every protein group identified and quantified by MaxQuant (604 distinct protein groups) with each protein group plotted on the x axis and the median SILAC value for that protein group plotted on the y axis. Two arbitrarily chosen thresholds are illustrated (black horizontal lines in left and right panels). B, representative ratio distribution plots. Data are plotted as a histogram with log2 SILAC ratios on the x axis and number of proteins for a given ratio on the y axis. Nonspecific contaminants reproducibly cluster in a Gaussian (normal) distribution centered at ∼0 (left panel), although the exact mean can deviate from 0 due to experimental variability as seen for the GFP-Pol2C data set (right panel). C, data from the GFP-Pol2C data set plotted with log2(M/L) SILAC ratio on the x axis and log2(H/M) SILAC ratio on the y axis with each point corresponding to the ratio value for a specific protein group. The bait protein is shown in red. Putative experimental contaminants (Experim. contamin.) cluster around the origin.

Mentions: A total of 709 and 696 protein groups were identified in GFP-Pol2C and Pol2A affinity purification experiments, respectively. Proteins labeled as _REV (non-real proteins from the reverse database) were automatically discarded as well as proteins that did not show any SILAC M/L, H/L, and H/M ratio. This yielded 604 protein groups for the GFP-Pol2C pulldown and 618 protein groups for the Pol2A endogenous IP. Average SILAC ratios for each remaining protein group were plotted in several ways to assess ratio distribution (see Fig. 2B) and changes in interactions between different conditions tested (see Fig. 2C).


Establishment of a protein frequency library and its application in the reliable identification of specific protein interaction partners.

Boulon S, Ahmad Y, Trinkle-Mulcahy L, Verheggen C, Cobley A, Gregor P, Bertrand E, Whitehorn M, Lamond AI - Mol. Cell Proteomics (2009)

Visualization of contaminant profiles and threshold levels. A representative example of a triple SILAC co-IP experiment using GFP-Pol2C as bait in cells either with or without α-amanitin treatment2 was used to generate the graphs shown. A, graphs showing median SILAC ratios for every protein group identified and quantified by MaxQuant (604 distinct protein groups) with each protein group plotted on the x axis and the median SILAC value for that protein group plotted on the y axis. Two arbitrarily chosen thresholds are illustrated (black horizontal lines in left and right panels). B, representative ratio distribution plots. Data are plotted as a histogram with log2 SILAC ratios on the x axis and number of proteins for a given ratio on the y axis. Nonspecific contaminants reproducibly cluster in a Gaussian (normal) distribution centered at ∼0 (left panel), although the exact mean can deviate from 0 due to experimental variability as seen for the GFP-Pol2C data set (right panel). C, data from the GFP-Pol2C data set plotted with log2(M/L) SILAC ratio on the x axis and log2(H/M) SILAC ratio on the y axis with each point corresponding to the ratio value for a specific protein group. The bait protein is shown in red. Putative experimental contaminants (Experim. contamin.) cluster around the origin.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2871420&req=5

Figure 2: Visualization of contaminant profiles and threshold levels. A representative example of a triple SILAC co-IP experiment using GFP-Pol2C as bait in cells either with or without α-amanitin treatment2 was used to generate the graphs shown. A, graphs showing median SILAC ratios for every protein group identified and quantified by MaxQuant (604 distinct protein groups) with each protein group plotted on the x axis and the median SILAC value for that protein group plotted on the y axis. Two arbitrarily chosen thresholds are illustrated (black horizontal lines in left and right panels). B, representative ratio distribution plots. Data are plotted as a histogram with log2 SILAC ratios on the x axis and number of proteins for a given ratio on the y axis. Nonspecific contaminants reproducibly cluster in a Gaussian (normal) distribution centered at ∼0 (left panel), although the exact mean can deviate from 0 due to experimental variability as seen for the GFP-Pol2C data set (right panel). C, data from the GFP-Pol2C data set plotted with log2(M/L) SILAC ratio on the x axis and log2(H/M) SILAC ratio on the y axis with each point corresponding to the ratio value for a specific protein group. The bait protein is shown in red. Putative experimental contaminants (Experim. contamin.) cluster around the origin.
Mentions: A total of 709 and 696 protein groups were identified in GFP-Pol2C and Pol2A affinity purification experiments, respectively. Proteins labeled as _REV (non-real proteins from the reverse database) were automatically discarded as well as proteins that did not show any SILAC M/L, H/L, and H/M ratio. This yielded 604 protein groups for the GFP-Pol2C pulldown and 618 protein groups for the Pol2A endogenous IP. Average SILAC ratios for each remaining protein group were plotted in several ways to assess ratio distribution (see Fig. 2B) and changes in interactions between different conditions tested (see Fig. 2C).

Bottom Line: The PFL is a dynamic tool that can be filtered for specific experimental parameters to generate a customized library.It will be continuously updated as data from each new experiment are added to the library, thereby progressively enhancing its utility.The application of the PFL to pulldown experiments is especially helpful in identifying either lower abundance or less tightly bound specific components of protein complexes that are otherwise lost among the large, nonspecific background.

View Article: PubMed Central - PubMed

Affiliation: The Wellcome Trust Centre for Gene Regulation and Expression, University of Dundee, Dundee DD1 5EH, Scotland, United Kingdom.

ABSTRACT
The reliable identification of protein interaction partners and how such interactions change in response to physiological or pathological perturbations is a key goal in most areas of cell biology. Stable isotope labeling with amino acids in cell culture (SILAC)-based mass spectrometry has been shown to provide a powerful strategy for characterizing protein complexes and identifying specific interactions. Here, we show how SILAC can be combined with computational methods drawn from the business intelligence field for multidimensional data analysis to improve the discrimination between specific and nonspecific protein associations and to analyze dynamic protein complexes. A strategy is shown for developing a protein frequency library (PFL) that improves on previous use of static "bead proteomes." The PFL annotates the frequency of detection in co-immunoprecipitation and pulldown experiments for all proteins in the human proteome. It can provide a flexible and objective filter for discriminating between contaminants and specifically bound proteins and can be used to normalize data values and facilitate comparisons between data obtained in separate experiments. The PFL is a dynamic tool that can be filtered for specific experimental parameters to generate a customized library. It will be continuously updated as data from each new experiment are added to the library, thereby progressively enhancing its utility. The application of the PFL to pulldown experiments is especially helpful in identifying either lower abundance or less tightly bound specific components of protein complexes that are otherwise lost among the large, nonspecific background.

Show MeSH