Install dependencies

pip install torchmetrics pytorch_lightning tool fair-esm

pip install "fair-esm[esmfold]"

pip install 'dllogger @ git+https://github.com/NVIDIA/dllogger.git'

pip install 'openfold @ git+https://github.com/aqlaboratory/openfold.git@4b41059694619831a7db195b7e0988fc4ff3a307'

pip3 install lightning

pip3 install tensorflow --user

pip3 install omegaconf

Re-install mkdssp using conda

conda install -c salilab dssp

Example notebook https://github.com/kopalgarg24/GraphBepi/blob/main/graphbepi.ipynb

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Intro

GraphBepi is a novel graph-based method for accurate B-cell epitope prediction, which is able to capture spatial information using the predicted protein structures through the edge-enhanced deep graph neural network.

We recommend you to use the web server of GraphBepi if your input is small.

System requirement

GraphBepi is developed under Linux environment with:

• python 3.9.12
• numpy 1.21.5
• pandas 1.4.2
• fair-esm 2.0.0
• torch 1.12.1
• pytorch-lightning 1.6.4
• (optional) esmfold

Software requirement

To run the full & accurate version of GraphBepi, you need to make sure the following software is in the mkdssp directory:
DSSP (dssp ver 2.0.4 is Already in this repository)

Build dataset

1. git clone https://github.com/biomed-AI/GraphBepi.git && cd GraphBepi
2. python dataset.py --gpu 0

It will take about 20 minutes to download the pretrained ESM-2 model and an hour to build our dataset with CUDA.

Run GraphBepi for training

After build our dataset BCE_633, train the model with default hyper params:

python train.py --dataset BCE_633

Run GraphBepi for prediction

1. Please execute the following command directly if you can provide the PDB file.
2. If you do not have a PDB file, you can use AlphaFold2 to predict the protein structure.

python test.py -i pdb_file -p --gpu 0 -o ./output

or

We have also deployed a faster structural prediction model ESMFold in our project, so you can process the sequences directly by following the commands below.

python test.py -i fasta_file -f --gpu 0 -o ./output

Web server, citation and contact

The GrpahBepi web server is freely available: interface

Citation:

@article{zengys,
  title={Identifying the B-cell epitopes using AlphaFold2 predicted structures and pretrained language model},
  author={Yuansong Zeng, Zhuoyi Wei, Qianmu Yuan, Sheng Chen, Weijiang Yu, Jianzhao Gao, and Yuedong Yang},
  journal={biorxiv},
  year={2022}
 publisher={Cold Spring Harbor Laboratory}
}

Contact:
Zhuoyi Wei (weizhy8@mail2.sysu.edu.cn) Yuansong Zeng (zengys@mail.sysu.edu.cn)