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 learning Filesrna_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引言 DeepRMSF是一种基于深度学习的自动化方法，可实现原子分辨率下RNA动态特性的「成像」分析。以指定的PDB格式RNA结构为初始输入，DeepRMSF首先将其转换为密度图，并基于原始结构提取密度图中的结构特征。随后将该密度图分割为一系列密度子块，作为模型的输入数据。最终，将预测得到的均方根波动（RMSF）子块合并为整体RMSF图，作为该RNA的RMSF预测图。 DeepRMSF源代码：该工具为一种基于深度学习的RNA动态建模自动化计算方法。 文件说明 rna_util.py：实现最基础的数据处理工作，例如生成模拟密度图。 rna_to_input.py：将密度图分割为子块，作为模型的输入数据。 model_util.py：包含模型训练所需的各类函数。 rmsf_model.py：实现DeepRMSF模型的核心代码。 rna_five_fold.py：采用五折交叉验证的方式对模型进行训练与测试。 main.py：使用方法 下载代码文件后，运行main.py即可启动工具。运行命令格式为：python main.py [-h] [--ori_dir ORI_DIR] [--data_dir DATA_DIR] [--box_file BOX_FILE] [--log_dir LOG_DIR] 可配置以下参数： --ori_dir：存储RNA的PDB格式文件的文件夹路径。 --data_dir：用于存储模拟密度图的文件夹路径。 --box_file：用于存储密度子块文件的文件夹路径。 --log_dir：用于存储预测结果的文件夹路径。 -h 或 --help：查看工具的帮助信息。 输入：RNA的PDB格式结构文件。 输出：替换了B因子（温度因子）的预测归一化RMSF值的PDB格式文件，文件命名格式为"{pdbid}_pre_nor.pdb"。此类PDB文件可通过Chimera等工具进行可视化处理。 示例：可通过查看测试文件夹，了解DeepRMSF的完整预测流程。 辅助软件 x3DNA-DSSR：用于获取RNA的二级结构信息。 UCSF Chimera（Chimera）：用于生成模拟密度图，该工具由加州大学旧金山分校生物计算、可视化与信息学资源团队开发，受美国国立卫生研究院P41-GM103311项目资助。 DeepRMSF