- Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability
Fusion of protein sequence and structural information, using denoising pre-training network for protein engineering (zero-shot). Please note that we do not require MSA or evolutionary information.
We focus on using end-to-end methods for protein directed evolution in zero sample scenarios, our overall framework can be shown in the following figure.
- [2024.4.28] Our paper is under review on eLife, 10.7554/eLife.98033.
- [2023.12.23] Our ensemble ProtSSN achieves a spearman score of 0.449 on ProteinGym v1.0. You can view and compare different baseline models on the ProteinGym website.
- ProteinGym: The pdb files folded by ColabFold 1.5 can be downloaded from https://huggingface.co/datasets/tyang816/ProteinGym_v1/resolve/main/ProteinGym_v1_AlphaFold2_PDB.zip
- ProteinGym: The pdb and mutant files in ProtSSN formation can be downloaded from https://huggingface.co/datasets/tyang816/ProteinGym_v1/resolve/main/ProteinGym_v1_ProtSSN.zip
- DTM and DDG dataset can be found in
data/DTManddata/DDG
We conducted tests on ProteinGym and compared it with the non-MSA method. Meanwhile, due to the fusion of structural information, we also conducted tests on two newly constructed stability datasets, DTm and DDG, in order to verify the correlation between structure and stability.
Our model surpasses most MSA methods and almost all language model methods without considering viruses, which means you can confidently use our model end-to-end. At the same time, because there is no need for MSA for reasoning or training, this reduces the tedious process and prior knowledge of collecting MSA, and there is no potential performance instability caused by MSA search methods/quantities.
Please make sure you have installed Anaconda3 or Miniconda3.
Enviroment.
conda env create -f environment.yaml
conda activate protssn
pip install torch_scatter torch_sparse torch_cluster -f https://data.pyg.org/whl/torch-2.3.0+cu121.html- For direct use of inference, we recommend at least 10G of graphics memory, such as RTX 3080
- For retraining a ProtSSN, we recommend that the larger the graphics memory. We recommend a 24GB RTX 3090 or better.
We have prepared 9 models that you can use separately or integrate. 😉
If you only want to only use one model for reasoning, we recommend using k20_h512.
mkdir model
cd model
wget https://huggingface.co/tyang816/ProtSSN/resolve/main/protssn_k20_h512.ptEnsembling models can achieve better results, and given our lower reasoning costs, you can use ensemble methods for zero-shot prediction. 😊
ProtSSN.zip contains all the model checkpoints, our training records and configs can be found in model/history and model/config.
wget https://huggingface.co/tyang816/ProtSSN/resolve/main/ProtSSN.zip
unzip ProtSSN.zip
rm ProtSSN.zipHere is a subset of ProtetinGym as the basic sample for mutation prediction in data/mutant_example, the files should be arranged in the following format:
data/proteingym-benchmark
|——DATASET
|——|——Protein1
|——|——|——Protein1.pdb
|——|——|——Protein1.tsv
|——|——Protein2
|——|——...
- Because our model requires structure (PDB) as input, we recommend using Alphafold for folding, and of course, ESMFold is also a good choice.
- If you do not have a tool installed for folding protein structures, you can search for your protein by Uniprot ID in the AlphaFold database (https://alphafold.ebi.ac.uk/) without consuming resources for folding.
The following script can be found at script/zeroshot_predict.sh, more details can be found at zeroshot_predict.py. We recommend using ensemble method for inference, which can be completed with a single RTX3080.
# k in (10, 20, 30)
# h in (512, 768, 1280)
# use single model for inference (default)
DATASET=proteingym-benchmark
CUDA_VISIBLE_DEVICES=0 python zeroshot_predict.py \
--gnn_model_name k10_h512 \
--mutant_dataset_dir data/mutant_example/$DATASET \
--result_dir result/$DATASET
# select the models for ensemble prediction
DATASET=proteingym-benchmark
CUDA_VISIBLE_DEVICES=0 python zeroshot_predict.py \
--gnn_model_name k10_h512 k20_h512 k30_h512 k10_h768 k20_h768 k30_h768 k10_h1280 k20_h1280 k30_h1280 \
--use_ensemble \
--mutant_dataset_dir data/mutant_example/$DATASET \
--result_dir result/$DATASETIn this case, we suggest that you start from a single point position, and you still need to organize your protein sequence and structural files in the same way as above.
You can use the following script to construct data for single point saturation mutations and use models for prediction.
python src/build_sav.py -d data/mutant_example/no_exp
# output: A0A5J4FAY1_MICAE contains 14193If you want to use ProtSSN for higher-level mutations, we recommend that you use a method that considers both rational design and AI design.
At the same time, the mutant column of the CSV or TSV data should be split by ":", for example, "M1V:A2V".
We provide how to fine-tune ProtSSN models on downstream tasks.
- You could prepare your own dataset formation in
data/finetune_example, the CSV file which contains label and corresponding pdb files are essential. - You can follow the
script/run_ft.shto fine-tune the ProtSSN model, we recommend the setting of k20_512.
For more downstream task dataset we recommend to use the datasets from SES-Adapter which we collect lots of dataset, such as EC, GO, MetalIonBinding, ProtSolM and other datasets with their pdb files, .
- PDB files can be downloaded at https://huggingface.co/datasets/tyang816/ProtFactory-PDB/tree/main.
- Please see my huggingface site to get task CSV files.
If you use the additional datasets we prepare, please cite the SES-Adapter, thanks!
@article{tan2024ses-adapter,
title={Simple, efficient and scalable structure-aware adapter boosts protein language models},
author={Tan, Yang and Li, Mingchen and Zhou, Bingxin and Zhong, Bozitao and Zheng, Lirong and Tan, Pan and Zhou, Ziyi and Yu, Huiqun and Fan, Guisheng and Hong, Liang},
journal={arXiv preprint arXiv:2404.14850},
year={2024}
}Perhaps you need protein embeddings for evolutionary or other direction analysis.
Here is a basic sample for embedding extraction in data/mutant_example, the files should be arranged in the following format:
data/proteingym-benchmark
|——DATASET
|——|——Protein1
|——|——|——Protein1.pdb
|——|——|——Protein1.tsv (no need)
|——|——Protein2
|——|——...
DATASET=proteingym-benchmark
CUDA_VISIBLE_DEVICES=0 python get_embedding.py \
--gnn_model_name k10_h512 k20_h512 \
--mutant_dataset_dir data/mutant_example/$DATASET \
--result_dir result/embed/$DATASETThe output file is <model>.pt which is the following format:
esm_embed is the embedding from facebook/esm2_650M, protssn_embed is the embedding from protssn which fusues the protein language and sturcture information.
{
protein1: {"esm_embed": esm_embed, "protssn_embed": protssn_embed},
protein2: {"esm_embed": esm_embed, "protssn_embed": protssn_embed},
...
}We use CATH for pre-training, you can find more details in https://www.cathdb.info/.
mkdir -p data/cath
cd data/cath
wget https://huggingface.co/datasets/tyang816/cath/blob/main/dompdb.tar
# or wget https://lianglab.sjtu.edu.cn/files/ProtSSN-2024/dompdb.tar
tar -xvf dompdb.tar
rm dompdb.tarYou can first create CATH graphs, but if you skip this step, the graphs will be automatically created in start training step.
see script/build_cath_dataset.sh
# k can be any number if you want
K=30
python src/data.py \
--data_type cath \
--c_alpha_max_neighbors $K \
--cath_dataset data/cath/cath_k$KYou can view our work as an adapter to enhance the capabilities of language models, so you can replace any language model as a prefix.
see script/run_pt.sh
K=20
H=512
CUDA_VISIBLE_DEVICES=0 \
python run_pt.py \
--seed 12345 \
--noise_ratio 0.05 \
--noise_type mut \
--gnn egnn \
--gnn_config src/config/egnn.yaml \
--gnn_hidden_dim $H \
--plm facebook/esm2_t33_650M_UR50D \
--cath_dataset data/cath/cath_k$K \
--c_alpha_max_neighbors $K \
--learning_rate 1e-4 \
--warmup_percent 0 \
--weight_decay 1e-4 \
--num_train_epochs 100 \
--batch_token_num 1024 \
--max_grad_norm 4 \
--gradient_accumulation_steps 10 \
--patience 20 \
--model_dir debug/model/"k"$K"_h"$HPlease cite our work if you have used our code or data for dry experiment testing/wet experiment directed evolution. We are pleased to see improvements in the subsequent work.
@article{tan2023protssn,
title={Semantical and Topological Protein Encoding Toward Enhanced Bioactivity and Thermostability},
author={Tan, Yang and Zhou, Bingxin and Zheng, Lirong and Fan, Guisheng and Hong, Liang},
journal={bioRxiv},
pages={2023--12},
year={2023},
publisher={Cold Spring Harbor Laboratory}
}