摘要
氨基甲酸乙酯(ethyl carbamate,EC)是一种主要存在于发酵食品和酒精饮料中对人体有潜在致癌性的有害物质,EC水解酶能够有效地消减EC,但其存在异源表达量较低、乙醇耐受性较差等问题。该研究结合计算机辅助改造与基于机器学习的融合标签设计等手段对赖氨酸芽孢杆菌来源的EC水解酶进行改造及优化。首先利用PROSS计算程序改造EC水解酶提高其乙醇耐受性,其次基于支持向量机回归的机器学习模型设计促溶标签提高其可溶性表达。基于PROSS筛选获得了组合突变体S21E/H197Y/Q328C/P348I(EC4),其酶活力相较于野生型提高了1.55倍,20%(体积分数)乙醇条件下的相对酶活力较野生型提高了约2.56倍。进一步筛选了合适的短促溶标签获得可溶性表达提高最多的是SVM1-EC4,其酶活力约为野生型的1.82倍,15%(体积分数)乙醇下的相对酶活力是野生型的3.99倍,且在模拟酒样中水解EC效果是野生型的2.07倍。总之,计算与融合标签相结合对EC水解酶进行改造能够有效地提高其可溶性表达及乙醇耐受性,为其工业应用提供了一定的理论依据和技术基础。
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.
作者
刘凤松
李亚杰
李亚贤
龙梦飞
陆智
LIU Fengsong;LI Yajie;LI Yaxian;LONG Mengfei;LU Zhi(Infinitus(China)Co.Ltd.,Guangzhou 510405,China;School of Biotechnology,Jiangnan University,Wuxi 214122,China)
出处
《食品与发酵工业》
CAS
CSCD
北大核心
2024年第3期59-66,共8页
Food and Fermentation Industries
基金
中国博士后科学基金项目(2022M711368)。