GPDL (Generative Protein Design by Language-model)

Protein Language Model Supervised Precise and Efficient Protein Backbone Design Method

GPDL is a deep learning method to design novel and high quality scaffold backbone given the desired motif residue topologies and sequences. Included in this code repository are two distinct methods, each offering a balance between generation speed and output quality.

💻 Environment set-up

Conda environment

# install esmfold and openfold 
conda create -n gpdl python=3.8
conda activate gpdl
conda install pip
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
conda install -c conda-forge biotite
pip install "fair-esm[esmfold]"
pip install 'dllogger @ git+https://github.com/NVIDIA/dllogger.git'
pip install 'openfold @ git+https://github.com/aqlaboratory/openfold.git@4b41059694619831a7db195b7e0988fc4ff3a307'

# install esm_if
conda create -n esm_if python=3.9
conda activate esm_if
conda install pytorch cudatoolkit=11.3 -c pytorch
conda install pyg -c pyg -c conda-forge
conda install pip
pip install biotite
pip install git+https://github.com/facebookresearch/esm.git

Third party source code

Our repo keeps a fork of ProteinMPNN in ./ProteinMPNN. Our conda environment is sufficient for running the ProteinMPNN codes to generate sequences compatible with our backbones.

git clone https://github.com/dauparas/ProteinMPNN.git

🔮 GPDL tutorial

GPDL takes three-steps module by inpaitning-fix bb design-hallucination in example.sh. Usually it needs 30 minutes for 100 backbones to generate protein scaffolds. Here is the bash parameters:

1. protein_name - Output path prefix
2. dir_name - Usually same as `protein_name
3. inpaint_seq - This defines the motif information format, like "x-y,Ax-y,m-n", where x,y indicate the length range of scaffold samples needed. For each design ,a random number N is sampled from $N \sim $ Uniform[x,y]. The motif position begins from A chain residue x to y residue y
4. mask_len - Defines the number of residues to be hallucinated within each specified range. For example, using --mask_len 10,20,20,20,10 along with --motif_id A92-99, A123-130, A47-54, A18-25 instructs the script to hallucinate sequences of 10, 20, 20, 20, and 10 residues in length, corresponding to and scaffolding around the specified residue ranges in chain A of the template.
5. motif_id - Identifies the residue ranges within the template that should remain fixed during hallucination.
6. max_mut- Maximum residues can be mutated.
7. step - MCMC iterations.

✏️ Citation

If you use the framework in your research, please cite the following paper.

@article {GPDL,
    Author={Bo, Zhang and Kexin, Liu and Zhuoqi, Zheng and Yunfeiyang, Liu and Ting, Wei and Haifeng, Chen},  
    title={Protein Language Model Supervised Precise and Efficient Protein Backbone Design Method},  
    year={2023},
    doi={10.1101/2023.10.26.564121},
    url={https://www.biorxiv.org/content/10.1101/2023.10.26.564121v1},
    journal={bioRxiv}
}

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Github codebase author : Bo Zhang, Kexin Liu, Zhuoqi Zheng

E-mail : {zhangbo777,lkxlkx,h2knight}@sjtu.edu.cn