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

Abstract excerpt

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...

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• Bacterial Proteins
• Glycolipids
• High-Throughput Screening Assays
• Molecular Docking Simulation
• Molecular Dynamics Simulation