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从序列到脚手架:从AlphaFold2预测的构建块计算设计的蛋白质纳米粒子疫苗

Cyrus M Haas^1,2,3, Naveen Jasti^2,4, Annie Dosey^2,5

¹Department of Chemical Engineering, University of Washington, Seattle, WA 98195.

Proceedings of the National Academy of Sciences of the United States of America

|November 3, 2025

概括

机器学习的进步使得新的蛋白质纳米粒子疫苗设计成为可能. 这种方法可以创建精确的,自我组装的纳米颗粒,引起强大的抗体对抗流感血素的反应.

科学领域:

生物技术是生物技术.
疫苗开发疫苗开发
计算生物学计算生物学

背景情况:

自组装蛋白质纳米粒子对下一代疫苗有希望,提供增强的抗体反应.
计算式蛋白质设计允许为特定的疫苗需求量身定制纳米粒子支架.
机器学习 (ML) 工具为克服蛋白质纳米粒子设计的局限性提供了新的可能性.

研究的目的:

开发一种可通用的方法,使用基于ML的工具来设计自组装蛋白质纳米粒子.
为了创建新的纳米粒子支架与原子级准确性用于疫苗应用.
基于针对流感的设计纳米粒子来设计一种功能性免疫原体.

主要方法:

借助ML工具,包括AlphaFold2,预测和设计寡合蛋白质构建块.
产生了六个60个子单位的蛋白质纳米粒子,具有二元象面对称性.
使用单粒子冷电子显微镜进行结构验证.
通过循环换,添加甘氨酸,并显示流感血凝素 (HA) RBD,设计了一个功能性免疫原体.

主要成果:

成功设计和结构验证了蛋白质纳米粒子,精确度达到原子水平.
创建了一个功能性免疫原体,显示一个稳定的HA受体结合域.
由此产生的免疫原诱导了小鼠的强有力的受体阻断和中和抗体反应.
在纳米粒子疫苗设计中展示了ML工具的实用性,扩大了自我组装蛋白质设计的起点.

结论:

基于ML的蛋白质建模工具对于设计纳米粒子疫苗来说实际上是有用的.
开发的方法显著扩大了用于自组装蛋白质设计的蛋白质构建块的范围.
这种方法有助于创建具有增强免疫原性的先进免疫原体.

关键词:

流感流感流感流感流感机器学习是机器学习.纳米颗粒是一种纳米粒子.蛋白质设计蛋白质设计疫苗,疫苗的使用情况.

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