BIDpred

Installation (In Ubuntu 18.04, 20.04)

1. Generate the conda environment

conda env create --file env.yaml --name BID
conda activate BID 

2. Install related packages

pip install wget
pip install biopython
pip install biotite
pip install fair-esm  
sudo apt-get install dssp  # for generating RSA using Biopython

3. Install models

# at first, it may take some time to download the esm-2, esm-if model  
import torch
_, _ = torch.hub.load("facebookresearch/esm:main", "esm2_t33_650M_UR50D") # load esm-2 model

import esm
_, _ = esm.pretrained.esm_if1_gvp4_t16_142M_UR50() # load esm-if model

BUG FIX from esm

ImportError: cannot import name 'esmfold_structure_module_only_8M' from 'esm.pretrained' (/home/{user}/anaconda3/envs/Bepitope/lib/python3.8/site-packages/esm/pretrained.py)

simply copy-paste the functions starting from esmfold_structure_module_only_8M into the pretrained.py (https://github.com/facebookresearch/esm/blob/2b369911bb5b4b0dda914521b9475cad1656b2ac/esm/pretrained.py#L274)

or

cp pretrained.py /home/{user}/anaconda3/envs/Bepitope/lib/python3.8/site-packages/esm/pretrained.py

Then everything will be ok.

Dataset

In cluster_msa_annotation, the dataset for train, test is available Each file name corresponds to (PDB_ID)(Antigen_chain)(Antibody_Hchain_Lchain).phy example. 1eo8_A_HL.phy, 3pnw_R_QP.phy

Data curation

1. Antigen sequences were clustered using mmseq2 by sequence identity 70%.
2. Within cluster (at least 4 elements), multiple sequence alignment (MSA) was generated using ClustalW
3. Epitopes were annotated in the MSA from antigen-antibody complex data (6 Angstrom)

*Representative sequence is on the first row
*Epitopes are annotated as capital letter while non-epitopes are not.

You can read the data using Biopython

from Bio import AlignIO
align = AlignIO.read(file_path, "phylip")

print(align[0].id)  # id of the first sequence in the alignment
print(align[0].seq)  # amino acid of the first sequence in the alignment

In Rep_Antigen_PDB, you can get the PDB file of Representative sequence in each MSA. Each file name corresponds to (PDB_ID)_(Antigen_chain).pdb

In csv, you can get the

• filtered_dataset.csv is data collected from SAbDab with filtering cutoff
• epitope_annotation.csv is annotated from Ab-Ag complex with 6 Angstrom distance
• train_csv contains immunodominance annotations of the training set (92 sets)
• test_csv contains immunodominance annotations of the test set (24 sets)

Prediction

# predict the epitopes from pdb (fetched)
python inference.py --pdb 1cfi

# for multiple inference... in bash
for pdb in pdb1 pdb2 pdb3 pdb4 pdb5 pdb6 ... pdb10
> do
> python inference.py --pdb $pdb
> done

# for inference using pdb file from local computer...
# the pdb file must be located in Custom_PDB(default) directory 
# or any directory you assign with --pdb_path
python inference_customPDB.py --pdb 6FNZ

Replication

# simply evaluate the trained model(in checkpoint directory) on the epitope3d test set (45 PDB)
python evaluate.py

# to train the models and save
# in model training, we recommend using GPU. CPU work is quite slow.
python train.py --model_save_dir models

# assign new directory with --model_checkpoint
# python evaluate.py simply evaluate the models saved in checkpoint
python evaluate.py --model_checkpoint models

Each python program contains more arguments. You can check with -h option

ex) python inference.py/inference_CustomPDB.py/train.py/evaluate.py -h