Phosphocholine cytidylyltransferase(CCT)is an important biocatalyst for citicoline(CDP-choline)production.However,it suffers from relatively low catalytic activity and halotolerance when it was applied in the one-pot ...Phosphocholine cytidylyltransferase(CCT)is an important biocatalyst for citicoline(CDP-choline)production.However,it suffers from relatively low catalytic activity and halotolerance when it was applied in the one-pot catalytic system coupling with the acetylphosphate based ATP regeneration system.A machine learning guided directed evolution approach was applied to simultaneously improve activity and halotolerance of Saccharomyces cerevisiae CCT(ScCCT),through random mutation on“hot-spot”regions predicted by selected models trained on different combinatory datasets generated by site-directed mutagenesis and random mutagenesis.The most desirable variant M3(P347S/P365L/K340T)exhibited an approximately 1.4 folds improvement in final titer of CDP-choline(182 mM)comparing with WT.This research proves that machine learning can improve effectiveness of random mutagenesis,and provides a possible solution to engineering membrane protein with complicated evolutionary fitness landscapes,which can be difficult for classic enzyme engineering approaches.展开更多
基金funded by the Jiangsu Province Outstanding Postdoctoral Program under grant number 379365.
文摘Phosphocholine cytidylyltransferase(CCT)is an important biocatalyst for citicoline(CDP-choline)production.However,it suffers from relatively low catalytic activity and halotolerance when it was applied in the one-pot catalytic system coupling with the acetylphosphate based ATP regeneration system.A machine learning guided directed evolution approach was applied to simultaneously improve activity and halotolerance of Saccharomyces cerevisiae CCT(ScCCT),through random mutation on“hot-spot”regions predicted by selected models trained on different combinatory datasets generated by site-directed mutagenesis and random mutagenesis.The most desirable variant M3(P347S/P365L/K340T)exhibited an approximately 1.4 folds improvement in final titer of CDP-choline(182 mM)comparing with WT.This research proves that machine learning can improve effectiveness of random mutagenesis,and provides a possible solution to engineering membrane protein with complicated evolutionary fitness landscapes,which can be difficult for classic enzyme engineering approaches.
