X-DeepGO

X-DeepGO is toolkit using deep learning for protein function annotation

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Installation

The sources for Deepfold can be downloaded from the Github repo.

You can either clone the public repository:

# clone project
git clone https://github.com/jianzhnie/X-DeepGO.git  
# First, install dependencies
pip install -r requirements.txt

Once you have a copy of the source, you can install it with:

python setup.py install

How to use

Single GPU Training

python main.py  \
--data_path ./protein \
--output-dir ./work_dir \
--lr 0.0001 \
--epochs 10 \
--batch-size 2 \
--log_wandb \
--workers 4

Distributed Training

torchrun --nnodes=1 --nproc_per_node=2  --rdzv_id=0 main.py  \
--data_path ./protein \
--output-dir ./work_dir \
--lr 0.0001 \
--epochs 10 \
--batch-size 2 \
--log_wandb \
--workers 4

Evaluate

## evaluate diamond
python  evaluate_diamondscore.py \
    --train-data-file ./protein/train_data.pkl \
    --test-data-file ./protein/test_data.pkl \
    --diamond-scores-file ./protein/test_diamond.res \
    --ontology-obo-file ./protein/go.obo \
    --output_dir ./work_dir


## evaluate model
python  evaluate_deepmodel.py \
    --train-data-file ./protein/train_data.pkl \
    --test-data-file ./protein/predictions.pkl \
    --terms-file ./protein/terms.pkl \
    --ontology-obo-file ./protein/go.obo \
    --output_dir ./work_dir

Inference

## inference
python inference_embedding.py  \
--data_path ./protein \
--output-dir ./work_dir \
--resume ./work_dir/ProtLM_esm_embedding_mean/model_best.pth.tar \
--model esm_embedding \
--pool_mode  mean \
--batch-size  128 \
--workers 4

Extract Embeddings

python extract_embeddings.py  \
--data_path ./protein \
--split "test" \
--batch-size 32

License

This library is licensed under the Apache 2.0 License.

Contributing to X-DeepGO

We are actively accepting code contributions to the X-DeepGO project. If you are interested in contributing to X-DeepGO, please contact me.

CNwangbin / X-DeepGO