Enzyme Catalysis and Immobilization

This study modifies arylmalonate decarboxylase to expand its substrate scope for synthesizing (R)-enantiomers of sterically hindered carboxylic acids with high enantiopurity.

This study investigates how polymer film thickness and charge density affect the activity of immobilized LacZ under acidic stress by controlling protonation depth and local pH buffering.

The paper uses droplet microfluidics to study the evolvability and robustness of ancestral and extant aryl sulfatases under varying mutation rates.

EnzFinder predicts promiscuous enzymes for chemical synthesis by analyzing reaction patterns, outperforming existing methods and enabling greener synthesis routes.

The paper engineers a single-component flavin-dependent tryptophan halogenase to enable regiospecific mono- and di-halogenation of tryptophan in one-pot reactions.

This study introduces CatOpt, a deep learning model that predicts enzyme optimal pH and designs point mutations to improve acid resistance, achieving a 7% activity increase in a specific enzyme.

The paper introduces POMPOMS, a method to create multifunctional protein microparticles by crosslinking biomolecular condensates, demonstrating controlled size and three functionalities.

The paper designs improved nucleoside 2'-deoxyribosyltransferase enzymes with enhanced catalytic efficiency and broad substrate promiscuity for generating diverse nucleoside products.

This paper develops an engineered PETase enzyme, DRK3, with improved thermal stability and PET degradation efficiency compared to existing enzymes.

The paper identifies a novel PET hydrolase from soil bacteria, enhances its activity with CALB and protein engineering, achieving 95.5% PET degradation.

This study investigates the activity-stability trade-off in PETase using simulations and experiments, revealing how reshaping loop dynamics can enhance both stability and activity.

The paper designs a panel of selective peroxygenases for terpene oxyfunctionalization using computational methods, resulting in improved enzymatic functions and new products.

The paper identifies and characterizes diverse nylon hydrolases, showing that 40% of tested enzymes have significant activity, with some showing substrate selectivity for Nylon 6,6 over Nylon 6.

This paper evolves a polyamidase through molecular dynamics simulations to improve its activity and stability for polyamide hydrolysis, but depolymerization remains limited.

The paper maps the substrate preferences and selectivity of 15 UPOs using 16 substrates, revealing high chemo- and regioselectivity and turnover numbers.

This study structures and engineers Omega transaminases from Pseudomonas putida to improve their efficiency in converting (R)-Phenylacetylcarbinol to (1R, 2S)-Norephedrine.

This study uses ancestral sequence reconstruction to explore PET-degrading cutinase sequence space, revealing diverse evolutionary pathways and the effectiveness of LASE in mapping fitness landscapes.

AI.zymes improves enzyme design by integrating multiple algorithms in an evolutionary framework, enhancing catalytic activity through iterative selection and design.

This paper introduces a continuous directed evolution method called GCCDE that automates enzyme engineering by linking enzyme activity to bacterial growth, demonstrating its effectiveness in improving CelB's low-temperature activity.

This study engineers Oxalate decarboxylase to improve its activity and stability for medical applications in hyperoxaluria treatment.

The paper develops a fluorogenic assay for NAD(P)H detection to enable high-throughput screening of biocatalysts in microdroplets, demonstrating improved sensitivity and applicability for glycosyl hydrolases and IREDs.

The paper engineers transmembrane alkane monooxygenases to improve terminal hydroxylation of isoprenyl acetate, enabling fermentative production of tulipalin A from renewable resources.

This paper develops a Saccharomyces cerevisiae-based whole-cell biocatalyst that completely depolymerizes PET plastic using a multi-enzyme cluster, achieving 4.9 mM TPA in seven days.

The paper demonstrates a computational strategy to design new PET hydrolases by replicating functional motifs from a template enzyme using deep learning and molecular computations, resulting in enzymes with high activity and thermostability.

This study develops a duo-enzyme system for PET degradation by immobilizing PETase and MHETase on magnetic nanoparticles, achieving higher product release and enzyme stability.

This study combines machine learning and high-throughput screening to design improved nuclease enzymes, finding a more effective variant than standard directed evolution methods.

This paper develops a hyper-porous hydrogel encapsulation method for microbial cells, demonstrating improved biocatalytic efficiency in co-culture fermentation.

This paper evolves a terminal deoxynucleotidyl transferase for enzymatic DNA synthesis using modified substrates, showing improved activity, template promiscuity, and thermostability.

The paper develops a machine learning-guided cell-free platform to accelerate enzyme engineering by mapping sequence-function relationships and optimizing enzymes for multiple chemical reactions.

This paper introduces ALDE, a machine learning-assisted directed evolution method that improves protein engineering outcomes by efficiently exploring epistatic mutation spaces.