README file to the project files provided as supporting information to the manuscript “A deep learning approach to the structural analysis of proteins” Dec. 30, 2018 Authors: Marco Giulini and Raffaello Potestio ================================== The dataset contains the following files: - datasets.zip: archive containing five .csv files, namely: - decoys_cm.csv : all the data for 10728 protein decoys, training set - evaluation_cm.csv : all data for 146 proteins in the evaluation set - random_CG.csv : 1200 Coulomb matrices. 100 CG models for each protein with 120 amino acids - 1e5g_centered_sphere.csv : 100 CG models in which the central atoms in 1e5g are not removed - 1e5g_random_sphere.csv : 10 CG models for 10 different (random) locations for the sphere that includes atoms that have to be retained. 100 CG models in total - decoys_labels.lab containing the labels associated to the 10728 decoys present in the training set - evaluation_labels.lab containing the labels associated to the 146 pdb files in the evaluation set - random_CG_labels.lab containing the labels associated to the 6 proteins with 120 amino acids - network_development_training: a python script that performs cross validation and full training of the model - saved_networks.zip FOLDER containing 10 networks: the architecture is included in .json files while weight parameters are inside .hs files - pdb_files.zip FOLDER containing the PDB files that have been employed in the project, namely: - pdb_files_len100 : pdb files with 100 amino acids - pdb_files_len101-110 : pdb files with a number of amino acids between 101 and 110 - decoys : decoys of length 100 extracted from the above folder: name syntax == PDBNAME_decoy_STARTRES_ENDRES.pdb EXAMPLE 6gsp.pdb will give rise to 6gsp_decoy_0_100.pdb , 6gsp_decoy_1_101.pdb , 6gsp_decoy_2_102.pdb , 6gsp_decoy_3_103.pdb , 6gsp_decoy_4_104.pdb - pdb_files_len100 : 6 pdb files with 120 amino acids

{"references": ["M. Giulini and R. Potestio, A deep learning approach to the structural analysis of proteins, Interface Focus 9 (2019) http://doi.org/10.1098/rsfs.2019.0003"]}