PDF(Pubmed)
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.
摘要：
人工智能（AI）技术在辅助抗体设计方面取得了长足的进步。然而,抗体设计仍然严重依赖于从血清中分离抗原特异性抗体，这是一个资源密集型和耗时的过程。为了解决这个问题，我们提出了一种预训练抗体生成大语言模型（PALM-H3），用于从头生成具有所需抗原结合特异性的人工抗体重链互补决定区3（CDRH3），减少对天然抗体的依赖。我们还建立了一个高精度模型抗原-抗体结合剂（A2binder），将抗原表位序列与抗体序列配对以预测结合特异性和亲和力。PALM-H3产生的抗体表现出与SARS-CoV-2抗原的结合能力，包括新兴的XBB变体，通过计算机内分析和体外测定证实。体外测定验证了PALM-H3产生的抗体对SARS-CoV-2野生型的刺突蛋白具有高结合亲和力和有效的中和能力，阿尔法,Delta,和新兴的XBB变体。同时,A2binder对各种表位和变体的结合特异性表现出优异的预测性能。此外，通过将Roformer架构中固有的注意力机制整合到PALM-H3模型中，我们提高了它的可解释性，提供对抗体设计的基本原理的重要见解。
