De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model

Haohuai He, Bing He (), Lei Guan, Yu Zhao, Feng Jiang, Guanxing Chen, Qingge Zhu, Calvin Yu-Chian Chen (), Ting Li () and Jianhua Yao ()
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Haohuai He: AI Lab, Tencent
Bing He: AI Lab, Tencent
Lei Guan: Xijing Hospital of Digestive Diseases
Yu Zhao: AI Lab, Tencent
Feng Jiang: AI Lab, Tencent
Guanxing Chen: Shenzhen Campus of Sun Yat-sen University
Qingge Zhu: Xijing Hospital of Digestive Diseases
Calvin Yu-Chian Chen: Peking University Shenzhen Graduate School
Ting Li: Xijing Hospital of Digestive Diseases
Jianhua Yao: AI Lab, Tencent

Nature Communications, 2024, vol. 15, issue 1, 1-19

Abstract: Abstract Artificial Intelligence (AI) techniques have made great advances in assisting antibody design. However, antibody design still heavily relies on isolating antigen-specific antibodies from serum, which is a resource-intensive and time-consuming process. To address this issue, we propose a Pre-trained Antibody generative large Language Model (PALM-H3) for the de novo generation of artificial antibodies heavy chain complementarity-determining region 3 (CDRH3) with desired antigen-binding specificity, reducing the reliance on natural antibodies. We also build a high-precision model antigen-antibody binder (A2binder) that pairs antigen epitope sequences with antibody sequences to predict binding specificity and affinity. PALM-H3-generated antibodies exhibit binding ability to SARS-CoV-2 antigens, including the emerging XBB variant, as confirmed through in-silico analysis and in-vitro assays. The in-vitro assays validate that PALM-H3-generated antibodies achieve high binding affinity and potent neutralization capability against spike proteins of SARS-CoV-2 wild-type, Alpha, Delta, and the emerging XBB variant. Meanwhile, A2binder demonstrates exceptional predictive performance on binding specificity for various epitopes and variants. Furthermore, by incorporating the attention mechanism inherent in the Roformer architecture into the PALM-H3 model, we improve its interpretability, providing crucial insights into the fundamental principles of antibody design.

Date: 2024
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DOI: 10.1038/s41467-024-50903-y

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