以病毒逃生为灵感的框架,用于结构引导的双诱蛋白生物传感器设计.
Yee Chuen Teoh1, Mohammed Sakib Noor2, Sina Aghakhani3
1Department of Computer Science, Iowa State University, Ames, Iowa, United States of America.
PLoS computational biology
|April 15, 2025
概括
一个新的计算平台,CTRL-V,通过预测病毒突变来设计选择性生物传感器. 它确定了负责免疫逃脱的关键SARS-CoV-2变种,并为更广泛的蛋白质设计应用提供了见解.
科学领域:
- 计算生物学是一种计算生物学.
- 蛋白质工程是一种蛋白质工程.
- 免疫学 免疫学 免疫学
背景情况:
- 在SARS-CoV-2尖端蛋白的受体结合域 (RBD) 进化以逃避抗体,同时保持ACE2受体结合.
- 现有的病毒逃脱预测器通常需要大量的数据,并且缺乏对各种蛋白质设计任务的概括性.
研究的目的:
- 介绍CTRL-V,这是一个可通用的计算平台,用于设计选择性结合 (双诱) 生物传感器蛋白.
- 使用SARS-CoV-2 RBD作为模型来证明CTRL-V在识别免疫逃避突变方面的能力.
主要方法:
- 代设计周期结合整数优化,随机抽样 (PyRosetta) 和深度学习 (ProteinMPNN).
- 利用公开可用的病毒逃逸数据作为通用计算工作流程的.
- 应用程序用于识别Raf激酶对Ras和Rap1a GTP有选择性结合的突变.
主要成果:
- CTRL-V确定了20%报告的SARS-CoV-2突变与特定抗体的免疫逃脱有关.
- 精确地确定了KP.2变种中约70%的单点突变,为免疫逃生机制提供了结构性的洞察力.
- 通过在Raf激酶中识别选择性GTP结合的关键突变部位,证明了通用性.
结论:
- CTRL-V为生物传感器设计提供了基于物理的,可通用的方法,克服了纯数据驱动方法的局限性.
- 该平台成功地模拟了免疫逃脱的病毒进化,并且可以适应其他蛋白质工程挑战.
- CTRL-V推进了选择性生物传感器的设计,其潜在应用超出了病毒学,包括酶-GTP相互作用.
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