PT  - JOURNAL ARTICLE
AU  - Shitong Luo
AU  - Yufeng Su
AU  - Xingang Peng
AU  - Sheng Wang
AU  - Jian Peng
AU  - Jianzhu Ma
TI  - Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models
AID  - 10.1101/2022.07.10.499510
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.07.10.499510
4099  - http://biorxiv.org/content/early/2022/08/09/2022.07.10.499510.short
4100  - http://biorxiv.org/content/early/2022/08/09/2022.07.10.499510.full
AB  - Antibodies are immune system proteins that protect the host by binding to specific antigens such as viruses and bacteria. The binding between antibodies and antigens are mainly determined by the complementarity-determining regions (CDR) on the antibodies. In this work, we develop a deep generative model that jointly models sequences and structures of CDRs based on diffusion processes and equivariant neural networks. Our method is the first deep learning-based method that can explicitly target specific antigen structures and generate antibodies at atomic resolution. The model is a “Swiss Army Knife” which is capable of sequence-structure co-design, sequence design for given backbone structures, and antibody optimization. For antibody optimization, we propose a special sampling scheme that first perturbs the given antibody and then denoises it. As the number of available antibody structures is relatively scarce, we curate a new dataset that contains antibody-like proteins as a complement to the original antibody dataset for training. We conduct extensive experiments to evaluate the quality of both sequences and structures of designed antibodies. We find that our model could yield highly competitive results in terms of binding affinity measured by biophysical energy functions and other protein design metrics.Competing Interest StatementThe authors have declared no competing interest.