conda create -n ppflow python==3.9
conda activate ppflow
# 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
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. 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
We provide the processed dataset of PPBench2024
through google drive: https://drive.google.com/drive/folders/1ce5DVmZz0c-p3PKrGDQoU_C9MD3cWLNq , 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: https://drive.google.com/drive/folders/1ce5DVmZz0c-p3PKrGDQoU_C9MD3cWLNq. 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
The raw data of PPBench2024 will be uploaded soon.
Run the following command for PPFlow training:
python train_ppf.py
Run the following command for DiffPP training:
python train_diffpp.py
python codesign_diffpp.py
python codesign_ppflow.py
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.
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 in 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
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 will lead to the directory looks 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
- Toos
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.
- bin
- TMscore
TMscore
TMscore.cpp
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.
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},
}