ExPfact is a statistically robust approach to estimate protection factors at resolution of the single amide from sparse and underdetermined HDX-MS data. Depending on the quality of the dataset analysed, ExPfact returns one or a discrete number of possible patterns of protection factors in agreement with experimental data.
If you use ExPfact in your work, please cite Skinner et al., 'Estimating constraints for protection factors from HDX-MS data' (2019)
ExPfact is licensed to academic users under the GNU GPL Version 2 licence. Commercial users should contact Dr Emmanuele Paci: e.paci@unibo.it.
Install Anaconda3.
Create a new environment using python version 3.6. Open the anaconda prompt and type:
conda create --name python36 python=3.6
conda activate python36
Install the dependencies (it is crucial to install the correct versions as shown below):
conda install numpy=1.16.1
conda install -c anaconda scipy=1.0.1
conda install -c anaconda cython
conda install pandas
pip install pyopenms
conda install -c conda-forge scikit-learn
Before running ExPfact, you also need to compile cython code:
cd python
python setup_calc_dpred.py build_ext --inplace
The clustering algorithm is implemented in the R package mclust. To install R:
conda install -c conda-forge r-base
Then, to install the package, run interactively R from the command line (just digit R), then:
install.packages("mclust")
It is highly recommended to accurately follow the tutorial on synthetic data before running ExPfact on experimental data. The tutorial is contained in the testing repository: please follow instructions shown in the readme file.