Welcome to the PyFDAP website! In FDAP experiments, a protein of interest is tagged with a photoconvertible fluorescent protein and expressed in vivo. The fluorescent fusion protein is then photoconverted, and the decrease in fluorescence intensity over time is monitored. The resulting intensity data is fitted with a decay function, and half-lives can be calculated from the fits.

Both intracellular and extracellular protein half-lives can be determined using FDAP. A static intracellular signal (e.g. Alexa488-dextran) can be used to create an intracellular mask, such that only intracellular pixels are considered when calculating intracellular intensity. The mask can be inverted to calculate extracellular intensities.

Here, we provide a standardized computational framework to analyze FDAP datasets. Our software PyFDAP features

  1. a comprehensive data format for handling, sorting, and annotating large FDAP datasets
  2. the capability to separate fluorescence intensities FDAP data sets into intra- and extracellular compartments based on counter-labeling
  3. established fitting algorithms
  4. a user-friendly environment
PyFDAP allows researchers from a non-computational background to easily evaluate FDAP data sets.