De novo design of a polycarbonate hydrolase

Efficient and easible biocatalysts: Strategies for enzyme improvement. A review

Owing to the environmental friendliness and vast advantages that enzymes offer in the biotechnology and industry fields, biocatalysts are a prolific investigation field. However, the low catalytic activity, stability, and specific selectivity of the enzyme limit the range of the reaction enzymes involved in. A comprehensive understanding of the protein structure and dynamics in terms of molecular details enables us to tackle these limitations effectively and enhance the catalytic activity by enzyme engineering or modifying the supports and solvents. Along with different strategies including computational, enzyme engineering based on DNA recombination, enzyme immobilization, additives, chemical modification, and physicochemical modification approaches can be promising for the wide spread of industrial enzyme usage. This is attributed to the successful application of biocatalysts in industrial and synthetic processes requires a system that exhibits stability, activity, and reusability in a continuous flow process, thereby reducing the production cost. The main goal of this review is to display relevant approaches for improving enzyme characteristics to overcome their industrial application.

Machine learning for functional protein design

Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.