Download Code & Install

Download Code

$ git clone https://github.com/jiaqingxie/DeepProtein.git
$ ###  Download code repository
$
$
$ cd DeepProtein
$ ### Change directory to DeepProtein

First time usage: setup conda environment

$ ## Build virtual environment with all packages installed using conda
$ conda create -n DeepProtein python=3.9
$
$ ##  Activate conda environment
$ conda activate DeepProtein
$
$ ## Install necessary packages
$ pip install git+https://github.com/bp-kelley/descriptastorus
$ pip install lmdb seaborn wandb pydantic DeepPurpose
$ ## Optional: install pytdc only if you need TDC-backed datasets such as TAP, CRISPR, IEDB, or SAbDab
$ conda install -c conda-forge pytdc
$
$
$ ## Choice 1: Torch 2.3.0 + CUDA Version 11.8
$ pip install torch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 --index-url https://download.pytorch.org/whl/cu118
$ pip install torch-geometric
$
$ ## Choice 2: Torch 2.3.0 + CPU
$ pip install torch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 --index-url https://download.pytorch.org/whl/cpu
$ pip install torch-geometric
$
$ pip install -r requirements.txt
$ conda deactivate ### exit

DeepProtein 2.0 removes DGL from the core installation path. Phases II and III add torch-geometric support for both single-protein and pair/PPI graph encoders through the PyG_* family, while legacy DGL_*, PAGTN, EGT, and Graphormer paths remain unsupported. Phase V further enables optional Laplacian positional encoding on the PyG_* path via compute_pos_enc=True.

Another Choice is to use python virtual env where it have saved plenty of space on package management.

$ ## cd to the expected path where you want to create such python env.
$ cd env_path
$
$ ## Build virtual python env.
$ python -m venv DeepProtein
$
$ ## You will find a folder named DeepProtein under your current env_path
$ ## Then you source the activate similar to conda activate:
$ source env_path/DeepProtein/bin/activate
$
$ ## Your will see sth. like (DeepProtein)(base) and this means the env is correctly activated
$ ## Install the packages as above
$
$ ## cd DeepProtein and pip install the rest packages
$ cd DeepProtein
$ pip install -r requirements.txt
$ deactivate ### exit

Second time and later

If you use conda env, then

$ conda activate DeepProtein
$ ##  Activate conda environment
$
$
$ deactivate ### exit

If you use python virtual env, then

$ source env_path/DeepProtein/bin/activate
$ ##  Activate python virtual environment where you saved it.
$ ##  In default we assume you use Linux / MacOS, otherwise remove "source"
$ ##  Just:
$ env_path/DeepProtein/bin/activate
$
$ deactivate ### exit