ProFOLD2 - A protein 3D structure prediction application
- Clone this repository and
cd
into it.
$git clone https://github.com/bigict/ProFOLD2.git
$cd ProFOLD2
$git submodule update --init # required if use FusedEvoformer, recommended.
- Create a virtual enviroment and install dependencies
$conda create -n profold2 python=3.11
$conda activate profold2
$bash install_env.sh
- Train a model
$python main.py train --prefix=OUTPUT_DIR
There are a lot of parameters, you can run
$python main.py train -h
for further help.
ProFOLD2
logs it's metrics to TensorBoard. You can run
$tensorboard --logdir=OUTPUT_DIR
Then open http://localhost:6006 in you browser.
- Inference
$python main.py predict --models [MODEL_NAME1:]MODEL_FILE1 [MODEL_NAME2:]MODEL_FILE2
Just like train
, you can run
$python main.py predict -h