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src
src
 
 
0-1_generate_msa.py
0-1_generate_msa.py
 
 
0-2_compute_weights.py
0-2_compute_weights.py
 
 
1-1_assign_RSA.py
1-1_assign_RSA.py
 
 
1-2_assign_MSA_properties.py
1-2_assign_MSA_properties.py
 
 
1-3_add_correlations.py
1-3_add_correlations.py
 
 
2-0_correlatons.py
2-0_correlatons.py
 
 
2-1_Neff_by_protein.py
2-1_Neff_by_protein.py
 
 
2-2_gprotein_stats.py
2-2_gprotein_stats.py
 
 
2-3_correlations_barplot_evol.py
2-3_correlations_barplot_evol.py
 
 
2-4-1_core_vs_surf_scatter.py
2-4-1_core_vs_surf_scatter.py
 
 
2-4-2_core_vs_surf_hist.py
2-4-2_core_vs_surf_hist.py
 
 
2-4-3_core_vs_surf_ddg_dependency.py
2-4-3_core_vs_surf_ddg_dependency.py
 
 
2-5_correlations_barplot_all.py
2-5_correlations_barplot_all.py
 
 
LICENCE
LICENCE
 
 
PARAMETERS.py
PARAMETERS.py
 
 
README.md
README.md
 
 

README.md

Exploring evolution to uncover insights into protein mutational stability

Some of the statistical analysis from the paper [1] are provided here.
Link: https://www.biorxiv.org/content/10.1101/2024.05.28.596203v2
[1] Hermans P., Tsishyn M., Schwersensky M, Rooman, M. & Pucci, F. (2024). Exploring evolution to uncover insights into protein mutational stability.

Usage

  • Install Python version 3.11 or later.

  • Install pip dependencies (numpy, scipy, biopython, matplotlib and seaborn).

  • In ./PARAMETERS.py, set the PATHS to all the required softwares and databases (described in the Method section of the main paper).

  • From the root directory ./analysis/, run all python scripts in the numbered order.

  • NOTE: All MSA and wieghts files are pre-computed and zipped in the ../msa/ folder, so you can just unzip them and skip execution of 0-*.py scripts.

  • NOTE: RSA values, secondary structure and MSA-base properties like CI, LOR and LORw are already pre-computed in the dataset files, so you can skip execution of 1-*.py scripts.

  • NOTE: Values from evolutionary models 'GEMME', 'EVcouplings' and 'pycofitness' are pre-computed and assigned to the dataset files. Runs of these programs can be replicated using their respective source code.

  • NOTE: Values of PoPMuSiC are pre-computed and assigned to the dataset files. Runs of PoPMuSiC can be replicated using our Web Server (free for academic uses) (https://soft.dezyme.com/login).

  • NOTE: To avoid eventual troubles with Copyright, we do not provide all computed values of structure-based ΔΔG predictors but only their correlation with the ΔΔG for each studied protein.