Enzyme Catalysis and Immobilization

This paper demonstrates the directed evolution of a multimodal plastic-degrading enzyme capable of breaking down both polyesters and polyurethane.

This study engineers HPD variants with enhanced NTBC tolerance to restore tyrosine metabolism while maintaining catalytic activity and MHC-II binding properties.

This study compares dual-enzyme spore display with mixed single-enzyme spores for cellulose hydrolysis, finding that mixing single-enzyme spores yields higher glucose production.

AlphaVariant uses reinforcement learning with evolutionary priors to optimize enzyme variants from sparse experimental data, achieving high activity improvements in multiple rounds of optimization.

Unattended droplet-microfluidic Bayesian optimization of a glycerol detection cascade matched design of experiments (DoE) with one third of the conditions; a second cascade used DoE with Raman readout.

This paper designs enantioselective photoenzymes using deep learning methods to precisely position a photosensitizer and substrate, achieving high stereocontrol and regiocontrol.

This paper introduces a computational pipeline for de novo metalloenzyme design using Metal Mer, achieving high selectivity and activity in catalytic reactions without directed evolution.

This paper develops engineered PLP-dependent biocatalysts for stereodivergent synthesis of non-canonical amino acids with complex stereochemical arrays, achieving high stereoselectivity and yield.

The paper identifies improved hydroxymethylfurfural oxidases through iterative computational methods combining molecular simulation and machine learning.

This study combines design and evolution to create new-to-nature enzymes capable of performing regio- and enantioselective C-H amination reactions for synthesizing chiral piperidines.

This paper introduces a photobiocatalytic three-component radical coupling method that enables the stereoselective assembly of congested stereocenters using evolved biocatalysts and photosensitizers.

This paper uses AI to engineer a more stable and pH-tolerant glucoamylase variant, achieving higher starch conversion without acidification.

This study shows that large-scale recombination can generate millions of catalytically competent PETases compatible with high-throughput screening.

This study reprograms binuclear copper enzymes to catalyze enantioselective radical (pseudo)halogenation reactions, demonstrating superior activity and selectivity compared to nonheme Fe enzymes.

This study identifies a conserved histidine residue critical for fluoroacetate dehalogenase activity on difluoroacetate and shows substitutions enhance its catalytic efficiency.

This study develops a machine learning framework to engineer enzymes by integrating sparse data with evolutionary and structural information, achieving improvements in catalytic performance, yield, and stability.

This paper introduces a physics-based framework for enzyme engineering that combines machine learning and molecular mechanics to predict catalytic rates and selectivity with high accuracy.

This paper introduces multistate enzyme design to optimize active sites across reaction coordinates, achieving higher catalytic efficiency and enantioselectivity than single-state designs.

Fluxion generates enzyme function predictions by modeling electron flow trajectories, enabling function generation beyond static classification.

This paper directly measures PET hydrolase interfacial kinetics on solid polymer surfaces, revealing catalysis-independent surface remodelling during enzyme binding.

Ancestral sequence reconstruction of a bacterial decarboxylase gave enzymes with up to 294-fold longer half-life at 45 °C, higher activity, and 99.7% ee on a vinyl malonate substrate.

This paper demonstrates an AI system that autonomously interacts with biological systems to discover new protein functions through continuous experimental feedback.

This study combines machine learning and directed evolution to optimize the monooxygenase HpaBC for improved L-Dopa production activity.

This paper describes a one-pot enzymatic method to produce CDP-ribitol at gram scale using a bifunctional fusion enzyme, achieving high yield and atom economy.

This study identifies a conformational switch in the catalytic lysine of DERA that governs its enantioselectivity, revealing a mechanism for stereochemical outcome in aldol addition.

The paper reports engineered sucrose synthase variants with enhanced activity and stability for efficient glycosylation, achieving high conversion yields and reduced environmental impact.

This paper uses machine learning to guide the creation of enzyme libraries with broader catalytic activity, showing improved performance across multiple reactions.

This paper introduces MLEE, a machine learning-assisted enzyme engineering workflow that improves hit rates for high-fitness enzyme variants compared to traditional methods.

The paper presents a framework for designing active enzymes from functional descriptions, achieving high catalytic activity in synthesized designs.