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机器学习辅助工程的光,氧,电压光感应器 Adduct 寿命

Stefanie Hemmer¹, Niklas Erik Siedhoff^2,3, Sophia Werner¹

¹Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf, Wilhelm Johnen Strasse, Jülich 52426, Germany.

|December 29, 2023

概括

机器学习通过预测可调节的暗恢复时间的变体来加速光氧电压 (LOV) 蛋白质的设计. 这促进了光遗传学和合成生物学工具的发展,使生物过程能够精确控制.

科学领域:

生物化学生化学
分子生物学分子生物学
合成生物学合成生物学

背景情况:

素结合,蓝光感应,光氧电压 (LOV) 光受体对于生物过程控制至关重要.
设计具有定制动力学的LOV光受体对于光遗传学和植物生物质生产等应用至关重要.
暗回收步骤,涉及 adduct 分裂,是速度限制和具有挑战性的工程由于其长时间和复杂性.

研究的目的:

为了应对工程方面的挑战,LOV光受体暗恢复动力学.
开发一种机器学习方法,用于预测具有微调暗恢复的LOV变体.
扩大LOV领域在合成 (光) 生物学中的实用性.

主要方法:

使用机器学习 (ML) 在有限的文献数据上进行训练.
采用数据生成和实验验证的代方法.
设计并测试了LOV域变体,具有更改的暗恢复特性.

主要成果:

成功预测了具有显著修改的暗恢复的LOV域变体.
在工程LOV变体的引入状态寿命中实现了7个数量级的跨度.
证明了ML的能力,以稀缺的数据和没有机械模型来指导蛋白质设计.

结论:

机器学习是蛋白质工程的一个可行的策略,特别是对于复杂的过程,如LOV光受体动力学.
这项工作为合成 (光) 生物学应用提供了优化的LOV工具.
机器学习方法克服了工程蛋白功能的传统方法的局限性.

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