蛋白质和抗体复合物的无监督进化与结构信息语言模型
Varun R Shanker1,2,3, Theodora U J Bruun2,3,4, Brian L Hie3,4
1Stanford Biophysics Program, Stanford University School of Medicine, Stanford, CA 94305, USA.
概括
将蛋白质结构整合到语言模型中可以增强蛋白质的设计和进化. 这种方法改善了针对SARS-CoV-2变种的抗体疗法,证明了蛋白质工程的强大方法.
科学领域:
- 计算生物学
- 蛋白质工程
- 结构生物学
背景情况:
- 大型语言模型 (LLM) 擅长仅从序列数据中学习蛋白质设计原理.
- 蛋白质的功能,活性和可变性是由它们的三维结构决定的,而不仅仅是序列.
- 现有的LLM通常缺乏全面蛋白质设计所需的结构上下文.
研究的目的:
- 用结构信息来指导蛋白质进化,开发一种通用蛋白质语言模型.
- 证明该模型在工程蛋白质复合物和改善治疗抗体方面的能力.
- 验证基于结构的蛋白质语言模型在没有特定任务培训的情况下增强蛋白质功能的有效性.
主要方法:
- 用蛋白质结构骨干坐标增强一个一般的蛋白质语言模型.
- 扩展ESM-IF1模型,最初用于单链结构,用于设计蛋白质复合体.
- 对抗SARS-CoV-2的两种治疗抗体的大约30种变体的查.
主要成果:
- 结构增强语言模型成功指导了各种蛋白质的蛋白质进化.
- 扩展ESM-IF1模型使得蛋白质复合体的工程成为可能.
- 对SARS-CoV-2变种BQ.1.1和XBB.1.5的抗体中和 (高达25倍) 和亲和力 (高达37倍) 进行了显著改善.
结论:
- 将结构信息整合到蛋白质语言模型中有利于确定有效的蛋白质进化途径.
- 这种方法可以在不需要特定任务的培训数据的情况下进行蛋白质工程.
- 这些发现为改善蛋白质设计和治疗开发铺平了道路,特别是针对病毒逃生变体的抗体.
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