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 creatinase, including 18 single‐point mutants, 22 double‐point mutants, 21 triple‐point mutants, and 12 quadruple‐point mutants. Using these data as inputs, we used a temperature‐guided protein language model, Pro‐PRIME, to learn epistatic features and design combinatorial mutants. After two rounds of design, we obtained 50 combinatorial mutants with superior thermostability, achieving a success rate of 100%. The best mutant, 13M4, contained 13 mutation sites and maintained nearly full catalytic activity compared to the wild‐type. It showed a 10.19°C increase in the melting temperature and an ~655‐fold increase in the half‐life at 58°C. Additionally, the model successfully captured epistasis in high‐order combinatorial mutants, including sign epistasis (K351E) and synergistic epistasis (D17V/I149V). We elucidated the mechanism of long‐range epistasis in detail using a dynamics cross‐correlation matrix method. Our work provides an efficient framework for designing enzyme thermostability and studying high‐order epistatic effects in protein‐directed evolution.