自主蛋白质工程的进步和批判性评估:朝着透明,可访问和可重复的平台
Konstantin Fg Weigmann1, Uwe T Bornscheuer1, Mark Doerr1
1Department of Biotechnology & Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, 17487 Greifswald, Germany.
Current opinion in biotechnology
|November 29, 2025
概括
蛋白质工程使用人工智能和自动化来实现更快的酶改进. 自主系统加速了设计-构建-测试-学习周期,将工程时间从几个月缩短到几天.
科学领域:
- 生物技术是生物技术.
- 生物化学 生物化学
- 计算生物学 计算生物学
背景情况:
- 蛋白质工程增强了酶特性,如活性和稳定性.
- 传统的设计-构建-测试-学习循环指导蛋白质的改进.
- 人工智能 (AI) 和自动化正在彻底改变这一过程.
研究的目的:
- 探索AI和自动化如何加速蛋白质工程.
- 要突出人工智能模型和自动化平台的整合.
- 讨论蛋白质设计中自主系统的潜力.
主要方法:
- 利用人工智能,包括零射击和监督模型,用于突变预测和变体设计.
- 实施自动化技术,如机器人液体处理器,用于大规模选.
- 开发闭环自主平台,用于集成设计,表达和测试.
主要成果:
- 人工智能模型使用测试数据预测有益的突变并改进设计.
- 自动化确保了可重复性,并使高通量变异选成为可能.
- 自主平台显著加快了从几个月到几天的多轮工程周期.
结论:
- 人工智能和自动化正在使蛋白质工程变得更加自主和数据驱动.
- 这些技术减少了对结构洞察力的依赖,并使得协同突变的发现成为可能.
- 分布式,符合FAIR的框架承诺协调,"自动驾驶"的实验室工作流程.
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