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README.txt
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README.txt
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This is the main README file for article "Computational prediction of the tolerance to amino-acid deletion in green-fluorescent protein" authored by Eleisha L. Jackson, Stephanie J. Spielman and Claus O. Wilke
The directory contains all of the associated data and scripts for this paper.
Contents:
data/
This directory contains all features for each deletion analyzed in the paper.
Contents:
egfp_functional_data.csv - Contains all functional data for the mutants
egfp_relax_model_scores.csv - Contains all of the energy scores for all protein mutant models
egfp_structural_data.csv - Contains all of the structure (WCN, RSA, SS) information for all mutants
machine_learning/
This directory contains the scripts that were used for the log regression and SVM part of the analysis.
plotting_scripts/
This directory contains a script (pca_plots.R) to plot the PCA of the data
r_scripts/
relax_regression_analysis.R - This a script that runs the logistic regression analysis analysis
t_tests.R - This is a script that performs t-test for structural properties
model_t_tests.R - This is a script that performs t-test for comparing model AUC distributions
rsa/
Contains scripts (mac_calc_rsa.py, mac_calc_sa.py) that calculate the Solvent Accessibility (SA) and Relative Solvent Accessibility (RSA) for the pdb 4EUL. It also contains the raw calculated RSA and SA values.
scripts/
This directory contains python scripts used in the analysis
renumber_pdb.py - A script that renumbers pdb files
summarize_scores.py - A script that calculates the mean score for each of the designed models
wcn/
Contains calc_wcn.py, the script used to calculate the Weighted Contact Numbers (WCN) for each residue and 4EUL_A_wcn.csv, a file containing the weighted contact number values (WCN)