The PEP2CCS is a deep learning model designed to predict CCS. This model integrates enhanced physical features of peptides, thereby improving the accuracy of CCS predictions. You can quickly get started with the PEP2CCS according to the following instructions.
Authors: Zhimeng Tian, Zizheng Nie, Yong Zhang, Daming Zhu* and Xuefeng Cui*
- *: To whom correspondence should be addressed.
Contact: [email protected]
Create virtual environment and install packages:
chmod +x run_setup.sh
bash ./run_setup.sh
conda activate PEP2CCSClone this repository by:
git clone https://github.com/xfcui/PEP2CCS.git
If you want to run our model on your own data, you need to provide the file.
After creating a virtual environment, you need to prepare data and trained model. We provide a sample data in the data directory. We also provide the trained model under the checkpoint/model.pt.
chmod +x run_prediction.sh
bash ./run_prediction.sh /path_to_test.csv# The test code is as follows:
chmod +x run_prediction.sh
bash ./run_prediction.sh ./src/data/test_data.csv
chmod +x ./src/Exp1/run.sh
bash ./src/Exp1/run.shchmod +x ./src/Exp2/run.sh
bash ./src/Exp2/run.shchmod +x ./src/Exp3/run.sh
bash ./src/Exp3/run.shchmod +x ./src/Exp4/run.sh
bash ./src/Exp4/run.shchmod +x ./src/Exp5/run.sh
bash ./src/Exp5/run.sh