README.md

TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation

If you like our project, please give us a star ⭐ on GitHub for latest update.

The official code for "TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation". Here we publish the inference code of TaxDiff. The training code & Protein sequence with Taxonomic lables dataset will be released after our paper is accepted.

💡 I also have other AI for Science projects that may interest you ✨.

ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
Liuzhenghao Lv, Zongying Lin, Li Hao, Yuyang Liu, Jiaxi Cui, Calvin Yu-Chian Chen, Li Yuan, Yonghong Tian

😮 Highlights

💡 Protein sequences Generation Model

• To the best of our knowledge, our TaxDiff is the first controllable protein generation model utilizing guidance from taxonomies.

🔥 Diffusion-based Framework

• TaxDiff proposes a taxonomic-guided framework that fits all diffusion-based protein design models. We also propose the patchify attention mechanism for better protein design.

⭐ Excellent performance

• Experiments demonstrate that our TaxDiff achieves state-of-the-art results in both taxonomic-guided controllable and unconditional protein sequence generation, excelling in structural modeling scores and sequence consistency.

🚀 Main Results

More detailed results can be found in our paper.

Unconditional Generation

Controllable Generation

📖 Data Preparation

For inference, please download from HuggingFace. Unzip it and put the ckpt into the folder ckpt/

ckpt/0012802_eval.ckpt

Our dataset can download from HuggingFace.

uniref50_200_256_clean_taxnomic_family_tid__filter_layer6.fasta

We will release protein sequences with taxonmic labels for training procedure once our paper is accepted.

If you want to select a specific protein taxonomic for your research, you need to first find his corresponding tax-id in the data_reader/Taxonnmic_classfication.xlsx, and then modify protein class lables in the sample_protein.py.

class_lables = torch.randint(low=1, high=int(23427), size=(1,num))

🛠️ Requirements and Installation

• Python == 3.10
• Pytorch == 2.2.0
• Torchvision == 0.17.0
• CUDA Version == 12.0
• Install required packages:

git clone git@[github.com/Linzy19/TaxDiff.git]
cd TaxDiff
pip install -r requirements.txt

🗝️ Inferencing

The inferencing instruction is in sample_protein.py.

python sample_protein.py --model DiT-pro-12-h6-L16 --cuda-num cuda:0 --num 500

✏️ Citation

If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.

@article{zongying2024taxdiff,
  title={TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation},
  author={Zongying, Lin and Hao, Li and Liuzhenghao, Lv and Bin, Lin and Junwu, Zhang and Yu-Chian, Chen Calvin and Li, Yuan and Yonghong, Tian},
  journal={arXiv preprint arXiv:2402.17156},
  year={2024}
}