Click-Chemistry-Based Free Azide versus Azido Sugar Detection Enables Rapid In Vivo Screening of Glycosynthase Activity

High-throughput screening of glycosynthases using azido sugars for oligosaccharides synthesis

Glycosynthases are mutant glycosyl hydrolases that can synthesize glycosidic bonds between acceptor glycone/aglycone groups and activated donor sugars with suitable leaving groups (e.g., azido, fluoro). However, it has been challenging to rapidly detect glycosynthase reaction products involving azido sugars as donor sugars. This has limited our ability to apply rational engineering and directed evolution methods to rapidly screen for improved glycosynthases that are capable of synthesizing bespoke glycans. Here, we outline our recently developed screening methodologies for rapidly detecting glycosynthase activity using a model fucosynthase enzyme engineered to be active on fucosyl azide as donor sugar. We created a diverse library of fucosynthase mutants using semi-random and random error prone mutagenesis and then identified improved fucosynthase mutants with desired activity using two distinct screening methods developed by our group to detect glycosynthase activity (i.e., by detecting azide formed upon completion of fucosynthase reaction); (a) pCyn-GFP regulon method, and (b) Click chemistry method. Finally, we provide some proof-of-concept results illustrating the utility of both these screening methods to rapidly detect products of glycosynthase reactions involving azido sugars as donor groups.

Quick and Accurate Estimates of Mutation Effects on Transition-State Stabilization of Enzymes from Molecular Simulations with Restrained Transition States

Data science and machine learning are revolutionizing enzyme engineering; however, high-throughput simulations for screening large libraries of enzyme variants remain a challenge. Here, we present a novel but highly simple approach to comparing enzyme variants with fully atomistic classical molecular dynamics (MD) simulations on a tractable timescale. Our method greatly simplifies the problem by restricting sampling only to the reaction transition state, and we show that the resulting measurements of transition-state stability are well correlated with experimental activity measurements across two highly distinct enzymes, even for mutations with effects too small to resolve with free energy methods. This method will enable atomistic simulations to achieve sampling coverage for enzyme variant prescreening and machine learning model training on a scale that was previously not possible.