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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)