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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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Overview of triple SILAC-based analysis of protein interaction partners. A, metabolic labeling of cells in culture using the triple SILAC approach can be used to detect specific protein interaction partners and dynamic changes in protein interactions under different biological conditions. Examples include comparing control conditions with (i) treatment with chemical inhibitors/stress etc., (ii) effect of mutations in the bait protein, or (iii) isoform-specific interactions. Light medium refers to normal environmental isotopes of carbon, nitrogen, and hydrogen, i.e.“unlabeled” 12C, 14N, and 1H, whereas medium and heavy media refer to cells grown in medium containing heavy isotope-labeled arginine (R) and lysine (K) as follows: medium, [13C6]arginine (R6) and 4,4,5,5-D4-lysine (K4); heavy, [13C6,15N4]arginine (R10) and [13C6,15N2]lysine (K8). B, overview showing the work flow in a representative triple SILAC analysis of protein interactions and their response to inhibitor treatment for either GFP-tagged or endogenous cell proteins. C, diagram illustrating the SILAC principle of differential labeling and how specific interacting proteins have higher ratios of heavy isotope-labeled peptides as compared with nonspecific contaminants. D, example of MS spectra for representative peptides illustrating a specific protein interaction partner (top), an internal contaminant binding nonspecifically to the beads (middle), and an external environmental contaminant, e.g. keratins (bottom). CTL, control; prot., protein; Ab, antibody; 1D, one-dimensional.
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Figure 1: Overview of triple SILAC-based analysis of protein interaction partners. A, metabolic labeling of cells in culture using the triple SILAC approach can be used to detect specific protein interaction partners and dynamic changes in protein interactions under different biological conditions. Examples include comparing control conditions with (i) treatment with chemical inhibitors/stress etc., (ii) effect of mutations in the bait protein, or (iii) isoform-specific interactions. Light medium refers to normal environmental isotopes of carbon, nitrogen, and hydrogen, i.e.“unlabeled” 12C, 14N, and 1H, whereas medium and heavy media refer to cells grown in medium containing heavy isotope-labeled arginine (R) and lysine (K) as follows: medium, [13C6]arginine (R6) and 4,4,5,5-D4-lysine (K4); heavy, [13C6,15N4]arginine (R10) and [13C6,15N2]lysine (K8). B, overview showing the work flow in a representative triple SILAC analysis of protein interactions and their response to inhibitor treatment for either GFP-tagged or endogenous cell proteins. C, diagram illustrating the SILAC principle of differential labeling and how specific interacting proteins have higher ratios of heavy isotope-labeled peptides as compared with nonspecific contaminants. D, example of MS spectra for representative peptides illustrating a specific protein interaction partner (top), an internal contaminant binding nonspecifically to the beads (middle), and an external environmental contaminant, e.g. keratins (bottom). CTL, control; prot., protein; Ab, antibody; 1D, one-dimensional.

Mentions: The combination of quantitative MS and differential labeling of proteins with heavy isotopes, especially stable isotope labeling with amino acids in cell culture (SILAC)1 (13, 14), can also help to distinguish between specific and nonspecific binding proteins in a co-immunoprecipitation (co-IP) experiment. This is achieved through the inclusion of an internal negative control, which allows for direct comparison between the relative levels of each protein present in the control and experimental samples (see Fig. 1). SILAC thus objectively identifies proteins that can bind nonspecifically, e.g. to the affinity matrix and/or the fusion tag, and highlights by comparison proteins that bind specifically to the bait protein (for reviews, see Refs. 15 and 16). We and others have used this isotope-based, quantitative MS approach to characterize both tagged and endogenous protein complexes in mammalian cells (17–20). Related differential isotope-based labeling strategies, combined with MS, have also been used to analyze specific binding proteins (21, 22).


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)

Overview of triple SILAC-based analysis of protein interaction partners. A, metabolic labeling of cells in culture using the triple SILAC approach can be used to detect specific protein interaction partners and dynamic changes in protein interactions under different biological conditions. Examples include comparing control conditions with (i) treatment with chemical inhibitors/stress etc., (ii) effect of mutations in the bait protein, or (iii) isoform-specific interactions. Light medium refers to normal environmental isotopes of carbon, nitrogen, and hydrogen, i.e.“unlabeled” 12C, 14N, and 1H, whereas medium and heavy media refer to cells grown in medium containing heavy isotope-labeled arginine (R) and lysine (K) as follows: medium, [13C6]arginine (R6) and 4,4,5,5-D4-lysine (K4); heavy, [13C6,15N4]arginine (R10) and [13C6,15N2]lysine (K8). B, overview showing the work flow in a representative triple SILAC analysis of protein interactions and their response to inhibitor treatment for either GFP-tagged or endogenous cell proteins. C, diagram illustrating the SILAC principle of differential labeling and how specific interacting proteins have higher ratios of heavy isotope-labeled peptides as compared with nonspecific contaminants. D, example of MS spectra for representative peptides illustrating a specific protein interaction partner (top), an internal contaminant binding nonspecifically to the beads (middle), and an external environmental contaminant, e.g. keratins (bottom). CTL, control; prot., protein; Ab, antibody; 1D, one-dimensional.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Overview of triple SILAC-based analysis of protein interaction partners. A, metabolic labeling of cells in culture using the triple SILAC approach can be used to detect specific protein interaction partners and dynamic changes in protein interactions under different biological conditions. Examples include comparing control conditions with (i) treatment with chemical inhibitors/stress etc., (ii) effect of mutations in the bait protein, or (iii) isoform-specific interactions. Light medium refers to normal environmental isotopes of carbon, nitrogen, and hydrogen, i.e.“unlabeled” 12C, 14N, and 1H, whereas medium and heavy media refer to cells grown in medium containing heavy isotope-labeled arginine (R) and lysine (K) as follows: medium, [13C6]arginine (R6) and 4,4,5,5-D4-lysine (K4); heavy, [13C6,15N4]arginine (R10) and [13C6,15N2]lysine (K8). B, overview showing the work flow in a representative triple SILAC analysis of protein interactions and their response to inhibitor treatment for either GFP-tagged or endogenous cell proteins. C, diagram illustrating the SILAC principle of differential labeling and how specific interacting proteins have higher ratios of heavy isotope-labeled peptides as compared with nonspecific contaminants. D, example of MS spectra for representative peptides illustrating a specific protein interaction partner (top), an internal contaminant binding nonspecifically to the beads (middle), and an external environmental contaminant, e.g. keratins (bottom). CTL, control; prot., protein; Ab, antibody; 1D, one-dimensional.
Mentions: The combination of quantitative MS and differential labeling of proteins with heavy isotopes, especially stable isotope labeling with amino acids in cell culture (SILAC)1 (13, 14), can also help to distinguish between specific and nonspecific binding proteins in a co-immunoprecipitation (co-IP) experiment. This is achieved through the inclusion of an internal negative control, which allows for direct comparison between the relative levels of each protein present in the control and experimental samples (see Fig. 1). SILAC thus objectively identifies proteins that can bind nonspecifically, e.g. to the affinity matrix and/or the fusion tag, and highlights by comparison proteins that bind specifically to the bait protein (for reviews, see Refs. 15 and 16). We and others have used this isotope-based, quantitative MS approach to characterize both tagged and endogenous protein complexes in mammalian cells (17–20). Related differential isotope-based labeling strategies, combined with MS, have also been used to analyze specific binding proteins (21, 22).

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