Advances in Zero‐Shot Prediction‐Guided Enzyme Engineering Using Machine Learning DOI Open Access
Chang Liu, Junxian Wu, Yongbo Chen

и другие.

ChemCatChem, Год журнала: 2024, Номер unknown

Опубликована: Дек. 23, 2024

Abstract The advent of machine learning (ML) has significantly advanced enzyme engineering, particularly through zero‐shot (ZS) predictors that forecast the effects amino acid mutations on properties without requiring additional labeled data for target enzyme. This review comprehensively summarizes ZS developed over past decade, categorizing them into kinetic parameters, stability, solubility/aggregation, and fitness. It details algorithms used, encompassing traditional ML approaches deep models, emphasizing their predictive performance. Practical applications in engineering specific enzymes are discussed. Despite notable advancements, challenges persist, including limited training necessity to incorporate environmental factors (e.g., pH, temperature) dynamics these models. Future directions proposed advance prediction‐guided thereby enhancing practical utility predictors.

Язык: Английский

Engineering an alcohol dehydrogenase from Gluconobacter oxydans for improved production of a bulky Ezetimibe intermediate DOI

Yuqinxin Xie,

Dongzhi Wei, Jinping Lin

и другие.

Molecular Catalysis, Год журнала: 2024, Номер 569, С. 114586 - 114586

Опубликована: Окт. 9, 2024

Язык: Английский

Процитировано

0
Advances in Zero‐Shot Prediction‐Guided Enzyme Engineering Using Machine Learning DOI Open Access
Chang Liu, Junxian Wu, Yongbo Chen

и другие.

ChemCatChem, Год журнала: 2024, Номер unknown

Опубликована: Дек. 23, 2024

Abstract The advent of machine learning (ML) has significantly advanced enzyme engineering, particularly through zero‐shot (ZS) predictors that forecast the effects amino acid mutations on properties without requiring additional labeled data for target enzyme. This review comprehensively summarizes ZS developed over past decade, categorizing them into kinetic parameters, stability, solubility/aggregation, and fitness. It details algorithms used, encompassing traditional ML approaches deep models, emphasizing their predictive performance. Practical applications in engineering specific enzymes are discussed. Despite notable advancements, challenges persist, including limited training necessity to incorporate environmental factors (e.g., pH, temperature) dynamics these models. Future directions proposed advance prediction‐guided thereby enhancing practical utility predictors.

Язык: Английский

Процитировано

0