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PFL Viewer Screenshot

Protein Frequency Library

The PFL assits with data analysis of immunoprecipitation experiments performed using a bead matrix. It aids in the detection of genuine interaction partners, especially low abundance, or loosly bound proteins that may be lost amongst the large, non-specific background. The predicted non-specific proteins can be used to normalise the values within a dataset to correct for experimental error.

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publications

Boulon & Ahmad et al. (2009)
Establishment of a protein frequency library and its application in the reliable identification of specific protein interaction partners
Molecular & Cellular Proteomics



PFL Cube

What is the Protein Frequency Library (PFL)?

  • The PFL viewer is a tool that is part of the PepTracker® data environment.

  • Library of proteins found in immunoprecipitation experiments using an affinity matrix.
  • Each protein is labelled with a frequency annotation describing how often it is detected within experiments.
  • Frequency can be calculated from all experiments or a subset of experiments filtered by parameters such as organism, cell type, affinity matrix type etc.
  • Proteins with low frequency of detection are more likely to be genuine interaction partners.
  • The predicted non-specific interaction partners can be used to normalise a dataset.

Protein-Protein Interactions

Protein Interactions

Identifying transient protein interactions can be difficult due to the binding affinities and stoichiometry of proteins. Furthermore, the background signal in immunoprecipitation experiments can constitute more than 80% of the identified proteins in any one dataset.

Often stringent experimental and computational analysis strategies are employed for reducing or eliminating background signals. However, these techniques can lead to reduced yields of recovered proteins or loss of signal from less abundant or lower affinity interaction partners.

The PFL allows for less stringent techniques to be used, by providing a method of discriminating between specific and nonspecific protein interactions.


How is the PFL constructred?

Data sent to cube

To generate the PFL, immunoprecipitation datasets from mass spectrometry experiments are extracted from the PepTracker® database. These datasets are then combined to generate a comprehensive library of all proteins identified in a range of experiments, employing different organisms, cell types, affinty matrix etc.

By counting the occurances of a protein across experiments, the PFL can generate an annotation describing the frequency of detection of a protein. It is assumed that proteins with a high frequency of detection are likely to be contaimants, including proteins that non-specifically bind to the affinity matrix or antibody, compared to low frequency, genuine interaction partners.


Applying the PFL to Your Datasets

Apply PFL to Dataset

After completion of an immunoprecipitation experiment, the proteins identified can be searched in the PFL for their respective frequencies. Effort can then be focused on proteins which show a low frequency, indicating a genuine interaction partner.

Furthermore, the PFL is dynamic and, therefore, can be filtered using a defined set of experimental conditions to obtain more refined frequency annotations. For example, if you have used a magnetic bead in your experiment then you may wish to include only magnetic bead experiments in your protein frequency analysis.

A major advantage of the PFL is that its prediction accuracy increases as additional experimental data are added to the database.


Normalisation of A Dataset

Normalisation of a Dataset

Experimental error can cause a shift in values measured within an experiment. When using the Stable Isotope Labelling by Amino Acids (SILAC) approach, relative ratio values are measured representing changes detected for proteins between conditions. Within an immunoprecipitation experiment, it is expected that non-specific interaction partners should not change between conditions. Hence, these proteins can be used as a basline to normalise a dataset.


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