Machine learning guided rational design of a non-heme iron-based lysine dioxygenase improves its total turnover number.

ABSTRACT: Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure-based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure-based machine-learning algorithm with classical molecular dynamics simulations to down select mutations for rational design of a non-heme iron-dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activit

SUBMITTER: Hunter Wilson R 

PROVIDER: S-EPMC11185610 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature