High-Throughput Screening and Mechanistic Elucidation of RhlA Mutants for Enhanced Rhamnolipid Biosynthesis Guided by EGCA-Net and Molecular Dynamics Simulations

RhlA serves as the crucial rate-limiting enzyme in rhamnolipid biosynthesis. In this study, we developed a fusion model named EGCA-Net, integrating a cross-attention mechanism with ESM-2 and graph convolutional network (GCN), to identify candidate RhlA mutants. Via an approach combining deep learning-based activity prediction, Rosetta analysis, molecular docking, and molecular dynamics simulations, four novel RhlA mutants (R74A_L148C_S173K, R74A_A101M_S173T, R74A_S173L_Q176L, and R74A_L148C_S173A) were screened from a targeted mutant library. Structural analyses revealed that these mutants form stable conformations, enhancing substrate binding affinity. In wet-lab validation, the candidate mutants exhibited superior catalytic potential, with the enzymatic activity of R74A_L148C_S173A reaching 373.38 U/mg, representing a 3.6-fold increase compared to the wild-type enzyme. The remaining mutants also maintained high activity levels (290-317 U/mg). In summary, this study provides an EGCA-Net-based screening framework for the rapid identification and in-depth characterization of novel enzyme mutants.