Optimizing enzyme thermostability by combining multiple mutations using protein language model.

ABSTRACT: Optimizing enzyme thermostability is essential for advancements in protein science and industrial applications. Currently, (semi-)rational design and random mutagenesis methods can accurately identify single-point mutations that enhance enzyme thermostability. However, complex epistatic interactions often arise when multiple mutation sites are combined, leading to the complete inactivation of combinatorial mutants. As a result, constructing an optimized enzyme often requires repeated rounds of design to incrementally incorporate single mutation sites, which is highly time-consuming. In this study, we developed an AI-aided strategy for enzyme thermostability engineering that efficiently facilitates the recombination of beneficial single-point mutations. We utilized thermostability data from

SUBMITTER: Bian J 

PROVIDER: S-EPMC11685841 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature