DeepRMSF-main

IntroductionDeepRMSF is an automated deep-learning based approach for ‘imaging’ the dynamics of RNA at atomic resolution. Starting with a given PDB-formatted structure, DeepRMSF, it is first translated to density map, structure feature in density map is translated by structure. The maps are segmented into a series of density boxes, which served as input for the model. Finally, predicted RMSF subboxes were then merged into an RMSF map as the RMSF prediction map for this RNA. Source code for DeepRMSF: an automated computational method for RNA dynamics modeling by deep learningFilesrna_util.pyThis file undergoes the most basic data processing, such as generating simulated maps.rna_to_input.pyThis file divides the maps into boxes as input to the model.model_util.pyFunctions required for model training.rmsf_model.pyDeepRMSF model.rna_five_fold.pyWe train and test model using 5-fold cross-validation.main.pyUsageDownload the code file and run main.py. python main.py [-h] [--ori_dir ORI_DIR] [--data_dir DATA_DIR] [--box_file BOX_FILE] [--log_dir LOG_DIR]You can enter the following parameters,--ori_dirThe folder for saving PDBs of RNAs.--data_dirThe folder where you want to save the simulated maps.--box_fileThe folder where you want to save the box_files.--log_dirThe folder where you want to save predicted data.-h or --helpYou can consult the help.InputPDBs of RNAs.OutputPDBs with predictive normalized RMSF values which replace B-factor values. The file names are "{pdbid}_pre_nor.pdb". These PDBs can be visualized by Chimera and so on.ExampleYou can view the test folder to learn about the DeepRMSF prediction process.Supporting softwaresx3DNA-DSSRTo obtain secondary structure.UCSF ChimeraSimulated maps were obtained with UCSF Chimera, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from NIH P41-GM103311.DeepRMSF