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)
- Stofella et al., 'High resolution hydrogen-deuterium protection factors from sparse MS data validated by NMR measurements' (2022)
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.