Sequence-Only Prediction of Binding Affinity Changes: A Robust and Interpretable Model for Antibody Engineering

Introduction

ProtAttBA is a protein language model that predicts binding affinity changes based solely on the sequence information of antibody-antigen complexes.

Usage

Install

1. Create conda environment

conda create -n protab python==3.10

2. Install environment dependency

# activate environment
source activate protab
# install pytorch
conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=11.8 -c pytorch -c nvidia 
(or use pip: pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118)

# install dependencies
pip install -r ./requirments.txt

dataset

The source data are located in the source_data(SKEMPI[1], AB-Bind[2]).

[1] Moal I H, Fernández-Recio J. SKEMPI: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models[J]. Bioinformatics, 2012, 28(20): 2600-2607.

[2] Sirin S, Apgar J R, Bennett E M, et al. AB‐bind: antibody binding mutational database for computational affinity predictions[J]. Protein Science, 2016, 25(2): 393-409.

Cross validation dataset is located in the cross_validation/data/csv folder (Using the dataset processed by: Jin et al., 2024). The results are located in cross_validation/results.

Sequence identity dataset is located in the seq-identity_sig-mul/data/identity_data folder (Use MMseqs with --min-seq-id 0.3). The results are located in the seq-identity_sig-mul/result_idt.

Single mutation training and multi-mutation testing dataset is located in the seq-identity_sig-mul/data/sigmul_data folder. The results are located in the seq-identity_sig-mul/result_sigmul.

Training

# For cross validation you can use cross_validation/scripts/bash_cross-validation.sh with different args
cp bash_cross-validation.sh ../
bash bash_cross-validation.sh 

# For Sequence identity you can use seq-identity_sig-mul/scripts/bash_seq_identity.sh with different args
cp bash_seq_identity.sh ../ 
bash bash_seq_identity.sh

# For Single mutation training and multi-mutation testing you can use seq-identity_sig-mul/scripts/bash_seq_sigmul.sh with different args
cp bash_seq_sigmul.sh ../ 
bash bash_seq_sigmul.sh

Evaluation

# For evaluation you can use the seq-identity_sig-mul/eval.py to predict the result by change the args
python eval.py