VAEResTL: A Novel Generative Model for Designing Complementarity Determining Region of Antibody for SARS-CoV-2

Saeed Khalilian, Zahra Moti, Arian Baloochestani, Yeganeh Hallaj, Alireza Chavosh, Zahra Hemmatian

2022

Abstract

The global impact of the COVID-19 pandemic underlines the importance of developing a competent machine learning (ML) approach that can rapidly design therapeutics and prophylactics such as antibodies/nanobodies against novel viral infections despite data shortage problems and sequence complexity. Here, we propose a novel end-to-end deep generative model based on convolutional Variational Autoencoder (VAE), Residual Neural Network (Resnet), and Transfer Learning (TL), named VAEResTL that can competently generate CDR-H3 sequences for a novel target lacking sufficient training data. We further demonstrate that our proposed method generates the third complementarity-determining region (CDR) of the heavy chain (CDR-H3) sequences for designing and developing therapeutic antibodies/nanobodies that can bind to different variants of SARS-CoV-2 despite the shortage of SARS-CoV-2 training data. The predicted CDR-H3 sequences are then screened and filtered for their developability parameters namely viscosity, clearance, solubility, stability, and immunogenicity through several in-silico steps resulting in a list of highly optimized lead candidates.

Paper Citation

in Harvard Style

Khalilian S., Moti Z., Baloochestani A., Hallaj Y., Chavosh A. and Hemmatian Z. (2022). VAEResTL: A Novel Generative Model for Designing Complementarity Determining Region of Antibody for SARS-CoV-2. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-552-4, SciTePress, pages 107-114. DOI: 10.5220/0010823700003123

in Bibtex Style

@conference{bioinformatics22,
author={Saeed Khalilian and Zahra Moti and Arian Baloochestani and Yeganeh Hallaj and Alireza Chavosh and Zahra Hemmatian},
title={VAEResTL: A Novel Generative Model for Designing Complementarity Determining Region of Antibody for SARS-CoV-2},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS},
year={2022},
pages={107-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010823700003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS
TI - VAEResTL: A Novel Generative Model for Designing Complementarity Determining Region of Antibody for SARS-CoV-2
SN - 978-989-758-552-4
AU - Khalilian S.
AU - Moti Z.
AU - Baloochestani A.
AU - Hallaj Y.
AU - Chavosh A.
AU - Hemmatian Z.
PY - 2022
SP - 107
EP - 114
DO - 10.5220/0010823700003123
PB - SciTePress