使用大型语言模型生成抗原特异性配对链抗体序列
Perry T Wasdin1,2,3, Nicole V Johnson4, Alexis K Janke3,5
1Program in Chemical and Physical Biology, Vanderbilt University Medical Center; Nashville, TN, USA.
bioRxiv : the preprint server for biology
|March 3, 2025
概括
人工智能 (AI) 推动了抗体的发现. 一个新的模型,MAGE (单克隆抗体生成器),产生了针对多种病毒的新型人类抗体序列,提高了效率和成功率.
科学领域:
- 生物技术是生物技术.
- 人工智能的人工智能
- 免疫学 免疫学 免疫学
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 (AI) 为抗体优化和生成提供了新的策略.
- 目标不可知抗体序列生成是一个新兴的研究领域.
研究的目的:
- 介绍MAGE (单克隆抗体生成器),一种用于产生抗体序列的新型AI工具.
- 微调基于序列的蛋白质语言模型 (PLM),用于生成配对的人类可变重链和轻链抗体序列.
- 为了证明MAGE在设计针对多个病毒点的抗体方面的能力.
主要方法:
- 开发MAGE,一种基于序列的蛋白质语言模型 (PLM).
- 精细调整PLM以满足生成配对的人类抗体序列的具体任务.
- 针对结合特异性的生成抗体序列的实验验证.
主要成果:
- MAGE成功地产生了新的和多样化的人类抗体序列.
- 实验验证了MAGE产生的抗体对抗SARS-CoV-2,H5N1禽流感和RSV-A的结合特异性.
- 证明了MAGE在没有起始模板的情况下设计针对多个目标的抗体的能力.
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.
- 该模型通过提高效率和成功率,显著提升了抗体发现.
- MAGE有望迅速开发针对新出现的传染病的治疗抗体.
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