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

Authors

Dongpei Wang, Chunming Xu, Yufei Yang, Ziyou Jiang, Xinyue Wang, Yujing Zhao, Ruiheng Li, Huijun Ma

Published in

Journal of agricultural and food chemistry. Jun 29, 2026. Epub Jun 29, 2026.

Abstract

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.

PMID:
42372267
Bibliographic data and abstract were imported from PubMed on 30 Jun 2026.

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