一个通用的平台,用于人工智能驱动的自主酶工程
Nilmani Singh1,2, Stephan Lane1,2,3, Tianhao Yu1,3,4
1Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
Nature communications
|July 2, 2025
概括
自主酶工程平台使用人工智能加速蛋白质设计. 这一新系统集成了机器学习和自动化,减少了对人类专业知识的需求,并使生物技术和可持续化学的快速进步成为可能.
科学领域:
- 生物技术是生物技术.
- 蛋白质工程是指蛋白质工程.
- 科学中的人工智能.
背景情况:
- 蛋白质是重要的分子机器,在能源,健康和可持续性方面具有广泛的应用.
- 当前的蛋白质工程方法往往是缓慢的,昂贵的,需要专门的知识.
- 为实际应用开发具有特定功能的新型蛋白质是一个重大挑战.
研究的目的:
- 开发一个适用于自主酶工程的通用平台.
- 消除对人类干预,判断和蛋白质工程领域专业知识的需求.
- 为了证明平台在设计具有增强功能的多种蛋白质方面的能力.
主要方法:
- 机器学习 (ML) 和大型语言模型 (LLM) 与生物基础自动化集成.
- 一个只需要输入蛋白序列和可量化的适应性测量的系统.
- 自主实验周期用于代蛋白质改进.
主要成果:
- 工程Arabidopsis thaliana化甲基转移酶 (AtHMT),基质偏好增加了90倍,乙基转移酶活性增加了16倍.
- 开发了一种Yersinia mollaretii植物酶 (YmPhytase) 变体,在中性pH下活性提高了26倍.
- 在四周的四轮中取得了这些结果,每种酶的特征不到500个变体.
结论:
- 自主酶工程平台显著加速了功能性蛋白质的开发.
- 这项技术使蛋白质工程民主化,使其超出专业领域.
- 该平台有可能在医学,生物技术,可再生能源和可持续化学领域取得快速进展.
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