ProtLGN is pre-trained on wild-type proteins for AA-type denoising tasks with equivariant graph neural networks to derive the joint distribution of the recovered AA types (red).

For a protein to mutate, the predicted probabilities suggest the fitness score for associated mutations (blue).

With additional mutation evaluations from wet biochemical assessments, the pre-trained model can be updated to better fit the specific protein and protein functionality (green).

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Please follow these simple example steps to get start! 😊

see requirements.txt for more detail.

We use the dataset from CATH 4.2, you can download from https://www.cathdb.info/.

cd <your dir>
wget 
mkdir -p data/cath_k10/raw

see script/build_cath_dataset.sh

see run_pretrain.sh

You can use your own checkpoint for zero-shot inference.

Data map:

|—— eval_dataset
|——|—— DATASET
|——|——|—— Protein1
|——|——|——|—— Protein1.tsv (DMS file)
|——|——|——|—— Protein1.pdb (pdb file)
|——|——|——|—— Protein1.fasta (sequence)
|——|——|—— Protein2
|——|——|——|...

see script/build_mutant_dataset.sh

see script/mutant_predict.sh

Please cite our paper:

@article {Zhou2023ProtLGN,
	author = {Bingxin Zhou and Lirong Zheng and Banghao Wu and Yang Tan and Outongyi Lv and Kai Yi and Guisheng Fan and Liang Hong},
	title = {Protein Engineering with Lightweight Graph Denoising Neural Networks},
	elocation-id = {2023.11.05.565665},
	year = {2023},
	doi = {10.1101/2023.11.05.565665},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2023/11/05/2023.11.05.565665},
	eprint = {https://www.biorxiv.org/content/early/2023/11/05/2023.11.05.565665.full.pdf},
	journal = {bioRxiv}
}

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Distributed under the MIT License. See LICENSE.txt for more information.

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