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PPFlow: Target-Aware Peptide Design with Torsional Flow Matching

Installation

Create the conda environment and activate it.

conda create -n ppflow python==3.9
conda activate ppflow

Install basic packages

# install requirements
pip install -r requirements.txt

pip install easydict
pip install biopython
# mmseq
conda install bioconda::mmseqs2

# Alternative: obabel and RDkit
conda install -c openbabel openbabel
conda install conda-forge::rdkit

# Alternative for visualization: py3dmol
conda install conda-forge::py3dmol

Packages for training and generating.

Install pytorch 1.13.1 with the cuda version that is compatible with your device. The geomstats package does not support torch>=2.0.1 on GPU until Mar.30, 2024. Here we recommend using torch==1.13.1.

# torch-geomstats
conda install -c conda-forge geomstats

# torch-scatter
pip install torch-scatter -f https://data.pyg.org/whl/torch-1.13.0+cu117.html  

# OR: stable torch-scatter
pip install ./temp/torch_scatter-2.1.1+pt113cu117-cp39-cp39-linux_x86_64.whl 

Dataset

We provide the processed dataset of PPBench2024 through google drive, together with processed `PPDBench'.

Please download data.zip and unzip it, leading to the data file directory as

- data
    - processed
        cluster_result_all_seqs.fasta
        cluster_result_cluster.tsv
        cluster_result_rep_seq.fasta
        parsed_pair.pt
        receptor_sequences.fasta
        split.pt
    - processed_bench
        cluster_result_all_seqs.fasta
        cluster_result_cluster.tsv
        cluster_result_rep_seq.fasta
        parsed_pair.pt
        receptor_sequences.fasta
        split.pt
    pdb_benchmark.pt
    pdb_filtered.pt

If you want the raw datasets for preprocessing, please download them through google drive. Unzip the file of datasets_raw.zip, leading to the directory as

- dataset
    - PPDbench
        - 1cjr
            peptide.pdb
            recepotor.pdb
        - 1cka
            peptide.pdb
            recepotor.pdb
        ...      
    - ppbench2024
        - 1a0m_A
            peptide.pdb
            recepotor.pdb

Training and Generating

Training from scratch

Run the following command for PPFlow training:

python train_ppf.py

Run the following command for DiffPP training:

python train_diffpp.py

For RDE finetuning, you should first download the pretrained RDE.pt model from the google drive, then save it as ./pretrained/RDE.pt, and finally, run the following command for finetuning:

python train_rde.py --fine_tune ./pretrained/RDE.pt

After training, you can choose an epoch for generating the peptides through:

python codesign_diffpp.py -ckpt {where-the-trained-ckpt-is}
python codesign_ppflow.py -ckpt {where-the-trained-ckpt-is}

Generating from pretrained checkpoints

Here we give the checkpoints that are pretrained, which is named ppflow_pretrained.pt and can be downloaded from the google drive. You can directly download it and copy it to ./pretrained/ppflow_pretrained.pt. Further, run the following to generation:

python codesign_diffpp.py -ckpt ./pretrained/ppflow_pretrained.pt

If you want to directly evaluate the peptides, we provide the peptides as codesign_results.tar.gz from our google drive, which consists of 100 samples / protein structure for more stable evaluation, with results given as

You should evaluate files that end with _bb3.pdb as the generated pdb, since the O element in _bb4.pdb is unstable in our reconstruction function.

Packages and Scripts for Evaluation

Packages for docking and other evaluation.

Vina: For Vina Docking, install the packages through:

 conda install conda-forge::vina
 pip install meeko
 pip install git+https://github.com/Valdes-Tresanco-MS/AutoDockTools_py3.git@aee55d50d5bdcfdbcd80220499df8cde2a8f4b2a
 pip install pdb2pqr

./tools/dock/vinadock.py gives an example of our Python interface for vinadock.

HDock: For HDock, firstly, libfftw3 is needed for hdock with apt-get install -y libfftw3-3. Besides, the HDock software can be downloaded through: http://huanglab.phys.hust.edu.cn/software/hdocklite/. After downloading it, install or unzip it to the ./bin directory, leading to the file structure as

- bin
    - hdock
        1CGl_l_b.pdb
        1CGl_r_b.pdb
        createpl
        hdock

./tools/dock/hdock.py gives an example of our python interface for hdock.

Pyrosetta: For pyrosetta, you should first sign up at https://www.pyrosetta.org/downloads. After the authorization of the license, you can install it through

 conda config --add channels https://yourauthorizedid:password@conda.rosettacommons.org 
 conda install pyrosetta   

./tools/relax/rosetta_packing.py gives an example of our python interface for rosetta side-chain packing.

FoldX: For FoldX, you should register and log in according to https://foldxsuite.crg.eu/foldx4-academic-licence, download the packages, and copy it to ./bin. Then, unzip it, which will lead the directory to look like

- bin
    - FoldX
        foldx

where foldx is the software. ./tools/score/foldx_energy.py gives an example of our Python interface for foldx stability.

ADCP: We provide the available ADFRsuite software in ./bin. If it is not compatible with your system, please install it through https://ccsb.scripps.edu/adcp/downloads/. Copy the ADFRsuite_x86_64Linux_1.0.tar into ./bin. Finally, the installed ADCP into ./bin should look like

- bin
    - ADFRsuite_x86_64Linux_1.0
        - Tools
          CCSBpckgs.tar.gz
          ...
      ADFRsuite_Linux-x86_64_1.0_install.run
      uninstall

Remember to add it to your env-path as

export PATH={Absolute-path-of-ppfolw}/bin/ADFRsuite_x86_64Linux_1.0/bin:$PATH

./tools/dock/adcpdock.py gives an example of our Python interface for ADCPDocking.

TMscore: The available TMscore evaluation software is provided in ./bin, as

- bin
    - TMscore
        TMscore 
        TMscore.cpp

PLIP: If you want to analyze the interaction type of the generated protein-peptide, you can use PLIP: https://github.com/pharmai/plip.

First, clone it to ./bin

cd ./bin
git clone https://github.com/pharmai/plip.git
cd plip
python setup.py install
alias plip='python {Absolute-path-of-ppfolw}/bin/plip/plip/plipcmd.py' 

./tools/interaction/interaction_analysis.py gives an example of our Python interface for plip interaction analysis.

Evaluation scripts

The evaluation scripts are given in ./evaluation directory, you can run the following for the evaluation in the main experiments:

# Evaluting the docking energy
python eval_bind.py --gen_dir {where-the-generated-peptide-is} --ref_dir {where-the-protein-pdb-is} --save_path {where-you-want-the-docked-peptide-to-be-saved-in}

# Evaluating the sequence and structure
python eval_bind.py --gen_dir {where-the-generated-peptide-is} --ref_dir {where-the-protein-pdb-is} --save_path {where-you-want-the-docked-peptide-to-be-saved-in}

We give an example file pair, so the gen_dir can be ./results/ppflow/codesign_ppflow/0008_3tzy_2024_01_19__19_16_21, ref_dir can be ./PPDbench/3tzy/ and the save_path can be ./results/ppflow/codesign_ppflow/0008_3tzy_2024_01_19__19_16_21.

Citation

If our paper or the code in the repository is helpful to you, please cite the following:

@inproceedings{lin2024ppflow,
	author = {Lin, Haitao and Zhang, Odin and Zhao, Huifeng and Jiang, Dejun and Wu, Lirong and Liu, Zicheng and Huang, Yufei and Li, Stan Z.},
	title = {PPFlow: Target-Aware Peptide Design with Torsional Flow Matching},
	year = {2024},
	booktitle={International Conference on Machine Learning},
	URL = {https://www.biorxiv.org/content/early/2024/03/08/2024.03.07.583831},
	eprint = {https://www.biorxiv.org/content/early/2024/03/08/2024.03.07.583831.full.pdf},
}