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通过使用EVOLVEpro的蛋白质语言模型进行快速的基导向进化

Kaiyi Jiang^1,2,3,4, Zhaoqing Yan^1,2,3, Matteo Di Bernardo⁵

¹Department of Medicine Division of Engineering in Medicine Brigham and Women's Hospital Harvard Medical School, Boston, MA, USA.

Science (New York, N.Y.)

|November 21, 2024

概括

通过结合蛋白质语言模型 (PLM) 和主动学习来加速蛋白质工程. 这种方法迅速增强了蛋白质的活性,克服了当前计算和实验方法的局限性.

科学领域:

生物技术
计算生物学
蛋白质工程

背景情况:

定向蛋白质进化对于生物医学应用至关重要,但面临复杂性和低效优化等挑战.
使用蛋白质语言模型 (PLM) 的现有in silico方法对蛋白质活动的概括和映射有限.

研究的目的:

使用最少的实验数据开发一种快速改善蛋白质活性的新框架.
克服当前蛋白质工程和指导方法的局限性.

主要方法:

推出了EVOLVEpro,一个简单的积极学习框架.
综合蛋白质语言模型 (PLM) 与适应性景观指导的回归模型.
用最少的数据对各种蛋白质工程任务应用框架.

主要成果:

在所需的蛋白质特性方面实现了多达100倍的改善.
在RNA生产,基因组编辑和抗体结合方面证明了六种蛋白质的有效性.
展示了少量激活学习对零射击预测的优势.

结论:

通过人工智能指导的蛋白质工程显著加速.
该框架为优化生物和医学中的蛋白质活动提供了强有力的方法.
突出了对蛋白质设计有限的实验数据的积极学习的优势.

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