Engineering the Substrate Specificity of Toluene Degrading Enzyme XylM Using Biosensor XylS and Machine Learning
Engineering the Substrate Specificity of Toluene Degrading Enzyme XylM Using Biosensor XylS and Machine Learning
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DOI：
10.1021/acssynbio.2c00577
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发表时间：
2023-02-03
影响因子：
4.7
通讯作者：
Ohnishi，Yasuo
中科院分区：
文献类型：
--
作者：
Ogawa，Yuki;Saito，Yutaka;Ohnishi，Yasuo
近年来，使用机器学习的酶工程已经发展起来。然而，为了获得大量的酶活性数据用于训练数据，有必要开发一种高通量和准确的方法来评估酶活性。在这里，我们研究了基于生物传感器的酶工程方法是否可以应用于机器学习。作为一个模型实验，我们的目的是修改的底物特异性的XylM，一个多步氧化反应中的速率决定酶XylMABC催化恶臭假单胞菌。XylMABC自然地将甲苯和二甲苯分别转化为苯甲酸和甲苯甲酸。我们的目标是改造XylM以提高其向非天然底物2，6-二甲酚的转化效率。野生型XylMABC将2，6-二甲苯酚轻微转化为3-甲基水杨酸，其是转录调节因子XylS inP的配体。putida。通过将荧光蛋白基因定位在与XylS结合的Pm启动子的控制下，产生XylS的大肠杆菌菌株以3-甲基水杨酸浓度依赖的方式显示出更高的荧光强度。我们使用传感器菌株的荧光强度作为指示剂来评估XylM变体的3-甲基水杨酸生产率。所获得的数据为XylM定向进化的机器学习提供了训练数据。两个机器学习辅助的定向进化循环导致获得XylM-D140 E-V144 K-F243 L-N244 S，其生产率比野生型XylM高15倍。这些结果表明，使用生物传感器的间接酶活性评估方法是足够定量和高通量的，可以用作机器学习的训练数据。这些发现扩展了机器学习在酶工程中的多功能性。
Enzyme engineering using machine learning has been developed in recent years. However, to obtain a large amount of data on enzyme activities for training data, it is necessary to develop a high-throughput and accurate method for evaluating enzyme activities. Here, we examined whether a biosensor-based enzyme engineering method can be applied to machine learning. As a model experiment, we aimed to modify the substrate specificity of XylM,a rate-determining enzyme in a multistep oxidation reaction catalyzed by XylMABC inPseudomonas putida. XylMABC naturally converts toluene and xylene to benzoic acid and toluic acid, respectively. We aimed to engineer XylM to improve its conversion efficiency to a non-native substrate, 2,6-xylenol. Wild-type XylMABC slightly converted 2,6-xylenol to 3-methylsalicylic acid, which is the ligand of the transcriptional regulator XylS inP. putida. By locating a fluorescent protein gene under the control of thePmpromoter to which XylS binds, a XylS-producingEscherichia colistrain showed higher fluorescence intensity in a 3-methylsalicylic acid concentration-dependent manner. We evaluated the 3-methylsalicylic acid productivity of XylM variants using the fluorescence intensity of the sensor strain as an indicator. The obtained data provided the training data for machine learning for the directed evolution of XylM. Two cycles of machine learning-assisted directed evolution resulted in the acquisition of XylM-D140E-V144K-F243L-N244S with 15 times higher productivity than wild-type XylM. These results demonstrate that an indirect enzyme activity evaluation method using biosensors is sufficiently quantitative and high-throughput to be used as training data for machine learning. The findings expand the versatility of machine learning in enzyme engineering.