Enzyme Catalysis and Immobilization

This study uses machine learning to guide directed evolution of PETase, resulting in mutants with higher optimal temperatures for PET degradation.

This paper demonstrates that encapsulating a de novo heme-containing enzyme in calcium-alginate beads enhances its catalytic activity in non-aqueous solvents through substrate partitioning and enables enzyme recovery.

This paper introduces a multi-agent directed evolution approach that identifies hidden mutations by using multiple substrates, improving enzyme engineering efficiency.

The paper discusses two systems for continuous directed evolution of enzymes and tests their adaptation for plant enzyme engineering.

This paper computationally redesigns PETase to improve its stability and PET degradation efficiency using the GRAPE strategy.

This study improves the specificity of the DszD enzyme through mutagenesis guided by structural predictions and simulations, resulting in increased enzyme activity.