Relating GPCR domains with functionality: receptor helix-bundle and C-terminus differentially influence GRK-specific functions and β-arrestin-mediated regulation
This code was written and used for analysis and visualisation of data contained in Matthees et al. All code was written and run in Python 3.10 as well as R 4.2.2.
A flowchart describing this process can be found in Suppl. Figure 2. The donut plot in this figure was created in donut_plot.R.
Conformational change data was acquired in a concentration-dependent manner as described in Matthees et al. Data was formatted and normalised to signal measured in the lowest ligand concentration for further processing in data_format.py.
Data was fitted using a four-parameter non-linear model and several fitting parameters were collected in fingerprint_fits.R.
Collected fit parameters were plotted to explore the possibility of certain populations separating concentration-dependent responses from signals which were not concentration-dependent in fit_categorization.R.
According to filter constrains choosen according to the previous results, responder and non-responder conditions were assigned in responsive_sensors_categorization.R. Additionally, after manual revision of the fitted plots 4x conditions were assigned as non-responsive.
For visualisation of the β-arrestin fingerprints, heatmaps were created in heatmap_generation.R.
A flowchart describing this process can be found in Suppl. Figure 10.
Tail- and core-transferability coefficient was calculated in coefficient_calculation.R and bubble plots illustrating the results were created in tail_core_coeff_plots.R.
To run code on your PC make sure that Python 3.10 as well as R 4.2.2. is installed. Download conformational change data and change 'path' variable to path of downloaded data before excuting code.