Data-driven strategies for the computational design of enzyme thermal stability: trends, perspectives, and prospects.

ABSTRACT: Thermal stability is one of the most important properties of enzymes, which sustains life and determines the potential for the industrial application of biocatalysts. Although traditional methods such as directed evolution and classical rational design contribute greatly to this field, the enormous sequence space of proteins implies costly and arduous experiments. The development of enzyme engineering focuses on automated and efficient strategies because of the breakthrough of high-throughput DNA sequencing and machine learning models. In this review, we propose a data-driven architecture for enzyme thermostability engineering and summarize some widely adopted datasets, as well as machine learning-driven approaches for designing the thermal stability of enzymes. In addition, we present a s

SUBMITTER: Dou Z 

PROVIDER: S-EPMC10160227 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature