Biosynthesis of D-allulose has been achieved using ketose 3-epimerases (KEases), but its application is limited by poor catalytic performance. In this study, we redesigned a genetically encoded biosensor based on a D-alluloseresponsive transcriptional regulator for real-time monitoring of D-allulose. An ultrahigh-throughput droplet-based microfluidic screening platform was further constructed by coupling with this D-allulose-detecting biosensor for the directed evolution of the KEases. Structural analysis of Sinorhizobium fredii D-allulose 3-epimerase (SfDAE) revealed that a highly flexible helix/loop region exposes or occludes the catalytic center as an essential lid conformation regulating substrate recognition. We reprogrammed SfDAE using structure-guided rational design and directed evolution, in which a mutant M3-2 was identified with 17-fold enhanced catalytic efficiency. Our research offers a paradigm for the design and optimization of a biosensor-based microdroplet screening platform.
D-Allulose is an attractive rare sugar that can be used as a low-calorie sweetener with significant health benefits. To meet the increasing market demands, it is necessary to develop an efficient and extensive microbial fermentation platform for the synthesis of D-allulose. Here, we applied a comprehensive systematic engineering strategy in Bacillus subtilis WB600 by introducing Dallulose 3-epimerase (DAEase), combined with the deactivation of fruA, levDEFG, and gmuE, to balance the metabolic network for the efficient production of D-allulose. This resulting strain initially produced 3.24 g/L of D-allulose with a yield of 0.93 g of Dallulose/g D-fructose. We further screened and obtained a suitable dual promoter combination and performed fine-tuning of its spacer region. After 64 h of fed-batch fermentation, the optimized engineered B. subtilis produced D-allulose at titers of 74.2 g/L with a yield of 0.93 g/g and a conversion rate of 27.6%. This D-allulose production strain is a promising platform for the industrial production of rare sugar.
d-Allulose, a rare sugar and functional sweetener,
can
be biosynthesized by d-allulose 3-isomerase (DAE). However,
most of the reported DAEs exhibit poor resistance under acidic conditions,
which severely limited their application. Here, surface charge engineering
and random mutagenesis were used to construct a mutant library of
CcDAE from Clostridium cellulolyticum H10, combined with high-throughput screening to identify mutants
with high activity and resistance under acidic conditions. The mutant
M3 (I114R/K123E/H209R) exhibited high activity (3.36-fold of wild-type)
and acid resistance (10.6-fold of wild-type) at pH 4.5. The structure–function
relationship was further analyzed by molecular dynamics (MD) simulations,
which indicated that M3 had a higher number of hydrogen bonds and
negative surface charges than the wild type. A multienzyme cascade
system including M3 was used to convert high-calorie sugars in acidic
juices, and functional juices containing 7.8–15.4 g/L d-allulose were obtained. Our study broadens the manufacture of functional
foods containing d-allulose.
Biosynthesis of D‐allulose has been achieved using ketose 3‐epimerases (KEases), but its application is limited by poor catalytic performance. In this study, we redesigned a genetically encoded biosensor based on a D‐allulose‐responsive transcriptional regulator for real‐time monitoring of D‐allulose. An ultrahigh‐throughput droplet‐based microfluidic screening platform was further constructed by coupling with this D‐allulose‐detecting biosensor for the directed evolution of the KEases. Structural analysis of Sinorhizobium fredii D‐allulose 3‐epimerase (SfDAE) revealed that a highly flexible helix/loop region exposes or occludes the catalytic center as an essential lid conformation regulating substrate recognition. We reprogrammed SfDAE using structure‐guided rational design and directed evolution, in which a mutant M3‐2 was identified with 17‐fold enhanced catalytic efficiency. Our research offers a paradigm for the design and optimization of a biosensor‐based microdroplet screening platform.
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