README.md

April 28, 2025 ยท View on GitHub

Running Steps

  1. Prepare Input Files Ensure you have prepared the target protein's PDB file (e.g. example.pdb). This file will serve as the basis for the input data.

  2. Data Preprocessing Use data_utils.py to extract structural information, SASA (Solvent Accessible Surface Area), and charge information from the PDB file, and save it as a CSV file.

  3. Modify Configuration Before running the prediction script, ensure the following settings are modified: Model Path: EMOCPD/models/model_save/best_6071model.pth.tar Input Data Path: Specify the path to the preprocessed CSV file. Output Path: Define where the prediction results should be stored.

    Open the predict.py file and modify the following code segment accordingly: MODEL_PATH = "EMOCPD/models/model_save/best_model.pth.tar" INPUT_CSV = "path/to/your/input.csv"
    OUTPUT_DIR = "path/to/your/output.csv"

  4. Run Prediction Run the predict.py script to generate the final prediction results.

    python predict.py

Output The prediction results will be saved in the specified output directory, typically as a CSV file, containing the following information: Predicted probabilities of amino acids at each position of the protein.