整合蛋白质语言模型和自动生物基础,以增强蛋白质进化.
Qiang Zhang1,2, Wanyi Chen1,3,4, Ming Qin1,5
1Zhejiang University, Hangzhou, Zhejiang, 310058, China.
Nature communications
|February 11, 2025
概括
本研究介绍了一种使用机器学习和生物基础技术的自动化蛋白质工程平台. 它迅速提高了酶活性,加速了工业用途的蛋白质进化.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质工程.
背景情况:
- 传统的蛋白质工程方法,如定向进化,是缓慢而劳动密集的.
- 机器学习和自动化生物工厂为优化蛋白质工程提供了新的途径.
- 自动化蛋白质工程平台可以加速发现新型蛋白质功能.
研究的目的:
- 开发一个闭环的,自动化的蛋白质工程平台.
- 将蛋白质语言模型 (ESM-2) 与自动化生物基础集成,以实现快速蛋白质进化.
- 提高工业应用蛋白质工程的速度和准确性.
主要方法:
- 实施了一个闭环设计-构建-测试-学习系统.
- 蛋白质语言模型ESM-2被用于蛋白质变异的零射击预测.
- 一个自动化的 biofoundry 构建和评估了变体.
- 一个多层感知子训练了一种适应性预测器,用于随后的变体选择.
主要成果:
- 在10天内完成了四轮自动进化,使用tRNA合成酶作为模型酶.
- 酶活性提高了多达2.4倍.
- 该平台在蛋白质进化过程中显著提高了速度和准确性.
结论:
- 开发的平台通过自动化和机器学习简化了蛋白质工程.
- 这种方法加速了用于各种应用的改进酶的发现.
- 自动化蛋白质进化对工业生物技术具有重大前景.
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