Integrated with HalluDesign, the CoDP model serves as a fast, distogram-based ranking tool. It leverages contrastive learning to quickly screen sequences by their predicted structural compatibility, facilitating a more efficient design cycle. This capability directly enhances the stability of monomeric proteins and improves the specificity of protein-ligand interactions.
We also implement modified LigandMPNN in our code.
if your already installed HalluDesign, you no need to install this package.
mamba create -n CoDP python==3.11
conda activate CoDP
# for Ligandmpnn
cd LigandMPNN
pip install -r requirements.txt
pip install transformers==4.49.0
bash get_model_params.sh "./model_params"
python CoDP_MPNN.py --input_file .pdb --output_dir ./test --mpnn ligand_mpnn --esmhead
CoDP_MPNN.py [-h] [--pdb_list PDB_LIST] [--input_file INPUT_FILE] [--fix_res_index FIX_RES_INDEX]
[--fix_chain_index FIX_CHAIN_INDEX] --output_dir OUTPUT_DIR [--num_seqs NUM_SEQS] [--esmhead]
[--mpnn MPNN] [--mpnn_temperature MPNN_TEMPERATURE]
We release our training code and dataset.
https://doi.org/10.5281/zenodo.19650179
python train_contact.py
python train_contractive.py
@article {Fang2025.11.08.686881,
author = {Fang, Minchao and Wang, Chentong and Shi, Jungang and Lian, Fangbai and Jin, Qihan and Wang, Zhe and Zhang, Yanzhe and Cui, Zhanyuan and Wang, YanJun and Ke, Yitao and Han, Qingzheng and Cao, Longxing},
title = {HalluDesign: Protein Optimization and de novo Design via Iterative Structure Hallucination and Sequence design},
elocation-id = {2025.11.08.686881},
year = {2025},
doi = {10.1101/2025.11.08.686881},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2025/11/09/2025.11.08.686881},
eprint = {https://www.biorxiv.org/content/early/2025/11/09/2025.11.08.686881.full.pdf},
journal = {bioRxiv}
}
@article{dauparas2023atomic,
title={Atomic context-conditioned protein sequence design using LigandMPNN},
author={Dauparas, Justas and Lee, Gyu Rie and Pecoraro, Robert and An, Linna and Anishchenko, Ivan and Glasscock, Cameron and Baker, David},
journal={Biorxiv},
pages={2023--12},
year={2023},
publisher={Cold Spring Harbor Laboratory}
}
@article{dauparas2022robust,
title={Robust deep learning--based protein sequence design using ProteinMPNN},
author={Dauparas, Justas and Anishchenko, Ivan and Bennett, Nathaniel and Bai, Hua and Ragotte, Robert J and Milles, Lukas F and Wicky, Basile IM and Courbet, Alexis and de Haas, Rob J and Bethel, Neville and others},
journal={Science},
volume={378},
number={6615},
pages={49--56},
year={2022},
publisher={American Association for the Advancement of Science}
}
The CoDP project, including both the source code and model weights, is licensed under the MIT License
LigandMPNN project (https://github.com/dauparas/LigandMPNN), is licensed under the MIT License