使用大型语言模型生成抗原特异性对链抗体
Perry T Wasdin1, Nicole V Johnson2, Alexis K Janke3
1Program in Chemical and Physical Biology, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Center for Computational Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Center for Antibody Therapeutics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Cell
|November 5, 2025
概括
人工智能 (AI) 推动了抗体的发现. 一种新型号MAGE (单克隆抗体生成器) 设计出针对多种病毒的新型人类抗体,如SARS-CoV-2,H5N1和RSV-A.
科学领域:
- 生物技术
- 免疫学
- 计算生物学
背景情况:
- 传统的抗体发现效率低,成本高,成功率低.
- 人工智能为抗体优化和生成提供了新的方法.
- 现有的AI方法可以以目标不可知的方式生成抗体序列.
研究的目的:
- 介绍MAGE (单克隆抗体生成器),一种用于生成抗体序列的新型AI工具.
- 微调基于序列的蛋白质语言模型 (PLM) 来生成配对的人类可变重链和轻链抗体序列.
- 证明MAGE在设计针对特定病毒目标的抗体方面的能力.
主要方法:
- 开发MAGE,一种基于序列的蛋白质语言模型 (PLM).
- 精确调整PLM以生成对对的人类可变重链和轻链抗体序列.
- 针对SARS-CoV-2,H5N1和RSV-A产生的抗体结合特异性的实验验证.
主要成果:
- MAGE成功生成了新的和多样化的抗体序列.
- 针对SARS-CoV-2,禽流感H5N1和RSV-A的MAGE生成抗体的结合特异性经过实验验证.
- 证明了MAGE在没有起始模板的情况下设计针对多个目标的人类抗体的能力.
结论:
- MAGE 是一流的人工智能模型,用于设计针对多个目标的人类抗体.
- 该模型克服了传统抗体发现方法的局限性.
- MAGE显示了针对性抗体疗法的快速发展潜力.
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