将罗塞塔序列设计与蛋白质语言模型预测相结合,使用进化规模建模 (ESM) 作为限制
Moritz Ertelt1,2, Jens Meiler1,2,3,4, Clara T Schoeder1,2
1Institute for Drug Discovery, University Leipzig Medicine Faculty, Liebigstr. 19, D-04103 Leipzig, Germany.
ACS synthetic biology
|April 3, 2024
概括
这项研究将机器学习语言模型与蛋白质设计工具相结合. 新方法通过使用进化见解来改进稳定和功能性蛋白质的设计.
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.
- 在生物信息学中的机器学习.
背景情况:
- 通过计算来设计稳定和功能性蛋白质是具有挑战性的,因为难以预测蛋白质动力学和质.
- 进化信息有助于蛋白质设计,重点关注与原生类似的序列,增强稳定性和功能.
- 最近的蛋白质语言模型擅长预测突变效应,但初步评估显示设计序列的得分较低.
研究的目的:
- 通过将蛋白质语言模型的预测整合到现有的设计协议中来增强计算蛋白质设计.
- 通过利用机器学习见解来提高计算设计蛋白质的稳定性和功能.
主要方法:
- 将进化规模建模 (ESM) 语言模型纳入罗塞塔蛋白质设计能量函数.
- 使用ESM预测开发了一种新的度量来限制设计期间的能量功能.
- 通过评估语言模型得分,序列恢复和罗塞塔能量来评估修改后的罗塞塔协议的性能.
主要成果:
- 与标准的罗塞塔设计相比,采用综合方法设计的序列获得了更高的语言模型分数.
- 新方法保持了类似的序列恢复率.
- 对于设计的序列,观察到适度的轻微下降,根据罗塞塔能量进行评估.
结论:
- 将蛋白质语言模型与罗塞塔设计工具箱相结合,为设计更稳定和功能更强的蛋白质提供了一种强大的方法.
- 这种整合利用了机器学习的预测能力和蛋白质设计软件的既定框架.
- 开发的方法代表了计算蛋白质序列设计领域的重大进展.
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