*** Quantification of GTPase cycling rates of GTPases and GTPase : effector mixtures using GTPase Glo assays ***
Authors: S. Tschirpke, W. K-G. Daalman, L. Laan
Delft University of Technology, Faculty of Applied Sciences, Department of Bionanoscience
Delft University of Technology, Kavli Institute of Nanoscience
Corresponding author: L. Laan
Contact Information:
l.laan@tudelft.nl
Delft University of Technology, Faculty of Applied Sciences, Department of Bionanoscience
Van der Maasweg 9, 2629 HZ Delft, The Netherlands


***General Introduction***
This dataset contains data and code generated for the publication 'Quantification of GTPase cycling rates of GTPases and GTPase : effector mixtures using GTPase Glo assays'.
The data in this data set was collected in the Laan lab of the Delft University of Technology - Faculty of Applied Sciences, Department of Bionanoscience, between 2022 and 2023.
This research project was made possible by funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement 758132) and funding from the Netherlands Organization for Scientific Research (Nederlandse Organisatie voor Wetenschappelijk Onderzoek) through a Vidi grant (016.Vidi.171.060). 


***Purpose***
The purpose of the data and code was to illustrate the analysis steps described in the accompanying publication. It describes step by step on two concrete examples how to analyse GTPase assay data generated with the 'GTPase-Glo assay' by Promega. The data consists of assay data for the GTPases Ras (example 1) and Cdc42 (example 2). The data can be used as a tutorial data set to follow the described protocol steps. The use of the data and code files are explained in detail in the accompanying publication. 


***Equipment***
All assays measured on a Synergy HTX plate reader (BioTek) in luminescence mode.
The code and output files were generated using matlab_R2022b software. 


***Description of the data and code in this data set***
The data set contains data as well as code referred to in the accompanying publication. 

example1.xlsx: This file contains 3 tabs: 'E1', 'E2', and 'matlab'. 
'E1' and 'E2' contain Luminescence data per well, and shows a simple analysis and plots of these data.
'matlab' contains the re-formatted data of 'E1' and 'E2', with the following headers:
Run: assay/experiment number
Time: incubation time
GTP_remaining: Amount of remaining GTP (normalised to the buffer, see below)
Error: error for the GTP_remaining values
Buffer_error: error of the buffer wells that were used for normalisation
Ras_conc: Ras concentration in uM. 

example2-matlab.xlsx: This file contains data, of the same format as in 'example1.xlsx'(tab: matlab), for Cdc42 and Cdc42-effector mixtures. The data is organised by protein mixture, which each tab containing data of assays of similar mixtures. 

 'Ras_example.ipynb': This is a python script that reads in data from 'example1.xlsx' tab 'E1', 'E2' and generates data shown in 'example1.xlsx' tab 'matlab'. The purpose of this script is to re-format data, its details are described in the accompanying publication. 


--- FOLDER: matlab_v4_2025-06.zip ---
This folder contains all data, functions, and output data needed for and generated with the matlab code. 
This folder contains additional sub-folders: 
* 'Additional functions': contains matlab functions needed to run the code
* 'Data': folder with required input data
* 'example1 matlab output': folder with output data generated with the code for example 1
* 'example2 matlab output': folder with output data generated with the code for example 2
* 'example1and2 matlab output': folder with output data generated with the code for combined analysis of example 1 and 2

The folder also contains the following files:
* 'Assaylist-example1.xlsx': required input file to run the code, used for example 1
* 'Assaylist-example2.xlsx': required input file to run the code, used for example 2
* 'Assaylist-example1and2.xlsx': required input file to run the code, used for combined analysis of example 1 and 2
* 'Process_assays.m': matlab file to run the code
* 'Plot_c_corr_histogram.m', Plot_pooled_values_std_err.m', 'Plot_rate_concentration.m', 'Plot_Semilog_GTP_time.m': plotting functions, for further data processing
* 'useful_colormaps.m': matlab function that contains the colour maps used for plotting

