从序列到脚手架:从AlphaFold2预测的构建块进行蛋白质纳米粒子疫苗的计算设计
Cyrus M Haas1,2,3, Naveen Jasti2,4, Annie Dosey2,5
1Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA.
bioRxiv : the preprint server for biology
|September 2, 2025
概括
机器学习推动了蛋白质纳米粒子疫苗的设计. 新的计算方法可以创建精确的,自我组装的蛋白质纳米粒子,增强强大的抗体反应的免疫效率.
科学领域:
- 生物化学
- 免疫学
- 计算生物学
背景情况:
- 自组装的蛋白质纳米粒子对于下一代疫苗至关重要, 提供优异的抗体反应.
- 计算式蛋白质设计使量身定制的纳米粒子架构成为可能,但目前的策略存在局限性.
研究的目的:
- 使用机器学习工具开发一种可通用的设计新型自我组装蛋白的方法.
- 为疫苗开发创造准确,功能性的纳米粒子免疫原体.
主要方法:
- 利用包括AlphaFold2在内的机器学习工具,从寡合构建块设计出新的自我组装蛋白质.
- 产生了六个具有icosahedral对称性的60个子单元蛋白质纳米粒子.
- 使用冷电子显微镜验证原子级设计的准确性.
- 通过循环 permutation 设计了一个纳米粒子免疫原体,添加一个 glycan,并显示一个流感血凝素受体结合域.
主要成果:
- 成功设计了六种具有icosahedral对称性的新型60个子单元蛋白质纳米粒子.
- 电子显微镜证实了三种设计的纳米粒子的原子级准确性.
- 在小鼠中引发了强烈的受体阻断和中和抗体反应.
结论:
- 基于机器学习的蛋白质建模工具对于设计纳米粒子疫苗非常有用.
- 开发的方法通过消除实验确定结构的需要,扩大了设计自我组装蛋白质的可能性.
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