Amino Acid Network

A Python Script to generate and analyze an Amino Acid Network and a Jupyter Notebook to create an Electrostatic Network.

Getting Started

To clone this repository, type

git clone https://github.com/lorpac/amino_acid_network.git

Prerequisites

The required packages are listed in the file requirements.txt. To install the requirements with pip, type:

pip install -r requirements.txt

You also need Rodigo Gilardi's class biographs. Clone it and do not forget to add the class folder (biograps/biographs) to you Python path. Alternatively, if you are using a pip virtual environment env, you can manually add it to your packages by saving a copy in the directory env/Lib/site-packages. To do so, move to the site-packages folder of your virtual environment or of your Python system install ad type

git clone https://github.com/rodogi/biographs .tmp;
mv .tmp/biographs .;
rm .tmp -f -r

If you are not familiar with virtual environments, here you will find the detailed instructions on how to create and manage virtual environments with pip.

Finally, you need to have LaTex installed on your system in order to produce the plots with Matplotlib. On Ubuntu, you can install LaTex and the necessary extensions by running

sudo apt-get install dvipng texlive-latex-base texlive-latex-extra texlive-fonts-recommended

and if you still get an error, try installing cm-super:

sudo apt-get install cm-super

For other operating systems, or if you encounter problems, please follow the instructions in Matplotlib's tutorial.

Amino Acid Network

Set-up the analysis

To set up the parameters of you analysis, you need to create a configuration file following the template of analysis_config_template.json and save it as analysis_config.json. In the configuration file, the following parameters must be given (in any order):

• name: a name of your analysis
• pdb_id: either a .pdb or .ent file name found in data/, or a valid PDB identifier. Note: in the second casem a internet connection will be needed to download the required pdb structure from the RCSB database.
• cutoff: cutoff distance to determine the connectivity between amino acids
• dim: type of links to consider. Valid options are: all (or an empty string, all links), 1D (between first neighbors in the sequence), 2D (between amino acids belonging to the same secondary structure element), 3D (between amino acids belonging to the same chain but to the different secondary structure elements), 4D (between amino acids belonging to different chains), 3-4D (3D and 4D), 1-2D (3D and 4D).
• select positions: boolean.
  • If select positions is true: start and stop are the sequence positions to start and stop the analysis
• variant: boolean. If true, the Perturbation Network is calculated
  • If variant is true, threshold is the difference threshold value to add links in the Peturbation Network and reference folder is the folder path where the WT Amino Acid Network is saved.
  • single_mutation: boolean.
    • If single_mutation is true, source is the mutation position in the sequence.
• subnet_highnw: boolean. If true, the subnetwork of nodes with high Nw value are drawn.
• subnet_highwij: boolean. If true, the subnetwork of links with high wij value are drawn.
• draw_neighborhoods: boolean. If true, draw the ego networks of each node.
• calculate_cliques: boolean. If true, the cliques of the Amino Acid Network are calculated.
  • draw_cliques: boolean. If true, the cliques of the Amino Acid Network are drawn.
• remove_hydrogen_atoms: boolean. If true, pre-process the PDB file to remove the hydrogen atoms.

Run the analysis

To run the analysis, type:

python analysis.py

You will find the results in the results/ folder.

Electrostatic Network

A jupyter notebook electrostatic_network.ipynb is provided to create the Electrostatic Network of a protein, starting from it Amino Acid Network. The instructions for the configuration of the analysis are provided within the notebook.

Authors

Lorenza Pacini - lorpac

How to cite

If you use the Amino Acid Network code in you work, please cite:

• Dorantes-Gilardi, R., Bourgeat, L., Pacini, L., Vuillon, L., and Lesieur, C. (2018). In proteins, the structural responses of a position to mutation rely on the Goldilocks principle: not too many links, not too few. Physical Chemistry Chemical Physics, 20(39), 25399-25410.

If you use the Electrostatic Network code in your work, please cite:

• Bourgeat L., Pacini L., Serghei A., and Lesieur C. (2021). Experimental diagnostic of sequence-variant dynamic perturbations revealed by broadband dielectric spectroscopy. Structure [under review].

Licence

The Amino Acid Network source code is available under the CeCILL licence. Please see LICENCE.txt for details.