Ethyl carbamate (EC) is a toxic substance to human beings existing in fermented food and alcoholic beverages. EC hydrolase can effectively reduce EC, however, which suffers low heterologous expression levels and poor ethanol tolerance. In this study, a combination of computer-assisted redesign and fusion tag optimization based on machine learning was employed to optimize and modify the EC hydrolase from Lysinibacillus fusiformis SCO2. Initially, the PROSS server was used to redesign the EC hydrolase to enhance its ethanol tolerance. Moreover, a support vector regression model (SVM) was used to design small peptide tags to improve soluble expression of EC hydrolase. A combinatorial variant, EC4 (S21E/H197Y/Q328C/P348I), was obtained, whose specific activity was 1.55 times higher than the wild-type, with approximately a 2.56-fold improvement under 20% ethanol. By further screening suitable short solubilizing labels, SVM1-EC4 with the highest soluble expression was obtained, of which enzyme activity was about 1.82 times that of wild-type. Besides, ethanol tolerance of SVM1-EC4 was 3.99 times that of wild-type under 15% ethanol. In simulated wine, EC hydrolysis capability of SVM1-EC4 was 2.07 times that of wild-type. In conclusion, computer-aided design and expression optimization effectively improve soluble expression and ethanol tolerance of EC hydrolase, which paves the way for its further application in industrial scale.
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