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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.

Show MeSH
Sun diagram and logical model of SILAC data. A logical model is presented in the form of a sun diagram illustrating the relationship between Measures and Dimensions captured in a SILAC experiment. The measures are typically numerical values from the experimental data, e.g.“number of peptides.” The dimensions define the various groupings (often hierarchical) by which users can aggregate the measures, e.g. cell type, date, cell extract, etc. id, identification.
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Figure 3: Sun diagram and logical model of SILAC data. A logical model is presented in the form of a sun diagram illustrating the relationship between Measures and Dimensions captured in a SILAC experiment. The measures are typically numerical values from the experimental data, e.g.“number of peptides.” The dimensions define the various groupings (often hierarchical) by which users can aggregate the measures, e.g. cell type, date, cell extract, etc. id, identification.

Mentions: When designing the multidimensional structure, the user model, which is defined by the users' understanding and perception of the data, was translated into a logical model. This logical model contained measures and dimensions. The measures are numerical values from the experimental data that are of interest to researchers, e.g. ratio, intensity, etc. The dimensions define the various groupings (often hierarchical) by which users can aggregate the measures, e.g. treatment, date, and cell cycle. The logical model was then represented as a sun model (see Fig. 3), which shows the measures in the center of the diagram and dimensions radiating from the center. The hierarchies in a dimension are symbolized by the levels marked along a dimension line. For example, the date dimension is hierarchical and has year, month, and day levels. In this study, we made use of the dimensions “bead type” and “cell extract” as filters to obtain customized PFLs.


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)

Sun diagram and logical model of SILAC data. A logical model is presented in the form of a sun diagram illustrating the relationship between Measures and Dimensions captured in a SILAC experiment. The measures are typically numerical values from the experimental data, e.g.“number of peptides.” The dimensions define the various groupings (often hierarchical) by which users can aggregate the measures, e.g. cell type, date, cell extract, etc. id, identification.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Sun diagram and logical model of SILAC data. A logical model is presented in the form of a sun diagram illustrating the relationship between Measures and Dimensions captured in a SILAC experiment. The measures are typically numerical values from the experimental data, e.g.“number of peptides.” The dimensions define the various groupings (often hierarchical) by which users can aggregate the measures, e.g. cell type, date, cell extract, etc. id, identification.
Mentions: When designing the multidimensional structure, the user model, which is defined by the users' understanding and perception of the data, was translated into a logical model. This logical model contained measures and dimensions. The measures are numerical values from the experimental data that are of interest to researchers, e.g. ratio, intensity, etc. The dimensions define the various groupings (often hierarchical) by which users can aggregate the measures, e.g. treatment, date, and cell cycle. The logical model was then represented as a sun model (see Fig. 3), which shows the measures in the center of the diagram and dimensions radiating from the center. The hierarchies in a dimension are symbolized by the levels marked along a dimension line. For example, the date dimension is hierarchical and has year, month, and day levels. In this study, we made use of the dimensions “bead type” and “cell extract” as filters to obtain customized PFLs.

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