Engineering the Substrate Specificity of Toluene Degrading Enzyme XylM Using Biosensor XylS and Machine Learning

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 in Pseudomonas 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 in P. putida. By locating
a fluorescent protein gene under the control of the Pm promoter to which XylS binds, a XylS-producing Escherichia
coli strain 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.