Project files provided as supporting information to the manuscript "Information-theoretical measures identify accurate low-resolution representations of protein configurational space"

<p>The dataset contains the following compressed folder:</p> <p>-Notebooks.zip:</p> <p>This folder contains:<br> &nbsp; -python_script:<br> &nbsp; &nbsp; &nbsp; &nbsp; -RESREL.py: script performing the clusterization and computing the relevance resolution curves<br> &nbsp; &nbsp; &nbsp; &nbsp; -random_curves.py: script generating the random value and computing the corresponding RES-REV curves_s<br> &nbsp; &nbsp; &nbsp; &nbsp; -Cluster_distance_matrix.py: script returning the distance among clusters for a given partition.<br> &nbsp; -python_notebook:<br> &nbsp; &nbsp; &nbsp; &nbsp; -Exploratory_analysis.ipynb: &nbsp;Analysis performed on the 12-protein_dataset<br> &nbsp; &nbsp; &nbsp; &nbsp; -DMAPS_ANTI.ipynb: Diffusion Map for the Antibody<br> &nbsp; &nbsp; &nbsp; &nbsp; -DMAPS_COV_1ake.ipynb: Diffusion Map + Inter-Intra state decomposition of covariance for 1ake</p> <p>Packages required for the usage of these python scripts/notebooks:<br> &nbsp; -numpy<br> &nbsp; -pandas<br> &nbsp; -matplotlib<br> &nbsp; -seaborn<br> &nbsp; -multiprocessing<br> &nbsp; -scipy</p> <p>&nbsp;</p> <p>========<br> RAW DATA<br> ========</p> <p>The raw data produced and employed in this study are available on a Google Drive folder at the following address:</p> <p>https://drive.google.com/drive/folders/1PasAUCgpR5-gdzUVEdyusgZIayQN0Le9</p> <p>In this folder, together with the compressed Notebooks.zip folder, one can fin the compressed folder&nbsp;Data.zip, within which the following data are present:</p> <p>-12-protein_dataset:<br> &nbsp; &nbsp; -md.mdp: the .mdp file used in the MD simulations<br> &nbsp; &nbsp; -PROTEIN_PDB_CODE:<br> &nbsp; &nbsp; &nbsp; &nbsp; -Hk_{sel}.npy &amp; Hs_{sel}.npy: the Rel &amp; Res curves, sel=[all, CA, CB]<br> &nbsp; &nbsp; &nbsp; &nbsp; -RMSD_{sel}.npy: the RMSD matrix, sel=[all, CA, CB]<br> &nbsp; &nbsp; &nbsp; &nbsp; -npt.gro:protein+water+ions structure @TEO the equilibration (NVT+NPT)<br> &nbsp; &nbsp; &nbsp; &nbsp; -MSR_df.csv: a dataset containing the following columns<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &#39;area&#39; : area behind the Relevance-Resolution curve;<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &#39;selection&#39;: the atomic selection ([&#39;all&#39;, &#39;CA&#39;, &#39;CB&#39;]) used to compute the RMSD matrix used for the clusterization (and consequently the Relevance-Resolution curves)<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &#39;method&#39;: the linkage measure used in the clustering procedure, an integer in [0,6];<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &#39;method_name&#39;: the linkage measure used in the clustering procedure, a string in [&#39;average&#39;,&#39;ward&#39;,&#39;complete&#39;,&#39;single&#39;,&#39;centroid&#39;,&#39;median&#39;,&#39;weighted&#39;];<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &#39;rmsd_mean&#39;: the mean value of the rmsd vector along the trajectory computed wrt the first frame;<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &#39;rmsd_var&#39;: the variance of the rmsd vector along the trajectory computed wrt the first frame;<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &#39;rgy_mean&#39;: the mean value of the radius of gyration &nbsp;along the trajectory;<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &#39;rgy_var&#39;: the variance of the radius of gyration &nbsp;along the trajectory;<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &#39;rmsf_mean&#39;: the mean value of the rmsf;<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &#39;rmsf_var&#39;: the variance of the rmsf;<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &#39;RMSD_M_mean&#39;: the mean value of the RMSD matrix.<br> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &#39;RMSD_M_var&#39;: the variance of the RMSD matrix.<br> &nbsp; -Random:<br> &nbsp; &nbsp; -curves.npy= 100K Relevance-Resolution Random curves for M=40001<br> &nbsp; &nbsp; -curves_s.npy= 100K Relevance-Resolution Random curves for M=15000<br> &nbsp; -validation_dataset:<br> &nbsp; &nbsp; -antibody:<br> &nbsp; &nbsp; &nbsp; &nbsp; -Hk_CB.npy &amp; Hs_CB.npy: the Rel &amp; Res curves<br> &nbsp; &nbsp; &nbsp; &nbsp; -RMSD_CB.npy: the RMSD matrix<br> &nbsp; &nbsp; &nbsp; &nbsp; -DIFF_{M}.npy: the eigenvalue/vector of the 10-D diffusion space<br> &nbsp; &nbsp; &nbsp; &nbsp; -Label_{method}.npy: the label vector for n_clusters<br> &nbsp; &nbsp; -1ake:<br> &nbsp; &nbsp; &nbsp; &nbsp; -Hk_{sel}.npy &amp; Hs_{sel}.npy: the Rel &amp; Res curves<br> &nbsp; &nbsp; &nbsp; &nbsp; -RMSD_{sel}.npy: the RMSD matrix<br> &nbsp; &nbsp; &nbsp; &nbsp; -DIFF_{M}.npy: the eigenvalue/vector of the 10-D diffusion space<br> &nbsp; &nbsp; &nbsp; &nbsp; -Label_{method}.npy: the label vector for n_clusters<br> &nbsp; &nbsp; &nbsp; &nbsp; -intra_{m}.npy: the intra-cluster covariance matrix<br> &nbsp; &nbsp; &nbsp; &nbsp; -inter_cov_{m}.npy: the inter-cluster correlation matrix</p> <p>&nbsp;</p> <p>NOTE<br> =====</p> <p>The matrices of the cluster distances for adenylate kinase and antibody have been computed through the script Cluster_distance_matrix.py.</p> <p>These matrices have not been included in the dataset because of their large size; the raw data are however available upon request.<br> &nbsp;</p>