A computational framework integrating a protein language model with alchemical simulation for gain-of-function enzyme design.
Engineering enzymes with enhanced activity and stability is a central goal of biotechnology, yet the inherent trade-off between optimizing global protein fitness and specific substrate binding affinity poses a significant challenge. Here, we present ESM-FEP, a computational framework that synergistically integrates a fine-tuned protein language model with alchemical free energy perturbation (FEP) to overcome this limitation. Our workflow employs a parameter-efficient fine-tuned ESM-2 model to perform high-throughput saturation mutagenesis, rapidly identifying mutations that preserve protein fitness. Top-ranking candidates are then subjected to rigorous FEP simulations to precisely quantify changes in substrate binding affinity. When applied to engineer the Zea mays dioxygenase ZmHSL1B for improved detoxification of the herbicide mesotrione, ESM-FEP efficiently navigated the mutational landscape and identified a quadruple mutant M5 (Q140H/Y205F/L332R/K336F). This variant demonstrated a catalytic efficiency approximately 7-fold higher than that of the wild-type enzyme, which was corroborated by in vitro assays and a detailed kinetic analysis. Furthermore, transgenic Arabidopsis thaliana expressing the engineered mutant M5 exhibited significantly enhanced herbicide tolerance, validating its functional efficacy in a biological context. The ESM-FEP framework establishes a generalizable and efficient strategy for the rational design of gain-of-function enzymes, with broad applications in biocatalysis, bioremediation, and precision agriculture.