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DualMPNN

This is the official implement of DualMPNN, harnessing structural alignment templates for protein sequence recovery using Dual-stream MPNNs.

image

Paper url: https://neurips.cc/virtual/2025/loc/san-diego/poster/118062

Setup

1. Setup Environment

a. You need to download foldseek implement locally and put it in foldseek diretory. It can be downloaded at https://github.com/steineggerlab/foldseek/releases

You should put the executable file in diretory of DualMPNN below.

📁 foldseek
└─ 📁 bin
    └─ 📄 foldseek (This is an executable file, about 700MB)

b. Install the conda environment by the following commands:

conda create -n DualMPNN python=3.9 numpy=1.26
conda activate DualMPNN
pip install -r requirements.txt

2. Setup foldseek

Enter the directory and download the template dataset from foldseek server:

cd foldseek
bin/foldseek databases PDB pdb tmp 

After downloading, process the dataset using this command:

bin/foldseek convert2pdb pdb PDBdb --pdb-output-mode 1

After this command, the foldseek is successfully setup in your environment.

The detailed information about foldseek commands please visit the official repo: https://github.com/steineggerlab/foldseek

3. Find Templates

The model takes constructed format as input. Given your dataset directory path, you could generate the formatted dataset by running the script template/findTemplate.py. This script will automatically find template using foldseek and generate .pt format file which can be directly utilized by train or test code.

See findTemplate.py for detailed usage.

You only need to generate the .pt format dataset once, unless you want to find different templates.

4. Train and Test

Run Dual_train.py script to train the model.

Run Dual_test.py script to test the model.

Citation

@inproceedings{
    liao2025dualmpnn,
    title={Dual{MPNN}: Harnessing Structural Alignments for High-Recovery Inverse Protein Folding},
    author={Xuhui Liao and Qiyu Wang and Zhiqiang Liang and Liwei Xiao and Junjie Chen},
    booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
    year={2025},
    url={https://openreview.net/forum?id=R42O6v84cX}
}

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Harnessing structural alignment templates for protein sequence recovery using Dual-stream MPNN

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