High-Throughput Screening and Mechanistic Elucidation of RhlA Mutants for Enhanced Rhamnolipid Biosynthesis Guided by...

Area of Science:

• Biotechnology
• Enzyme Engineering
• Computational Biology

Background:

• Rhamnolipid biosynthesis is critical for various industrial applications.
• RhlA is the rate-limiting enzyme in this pathway, making it a key target for improvement.
• Enhancing RhlA activity can significantly boost rhamnolipid production.

Purpose of the Study:

• To develop a novel computational framework for identifying high-activity RhlA mutants.
• To engineer RhlA variants with superior catalytic efficiency for rhamnolipid production.
• To validate the efficacy of the developed computational model through experimental methods.

Main Methods:

• Development of EGCA-Net, a fusion model combining cross-attention, ESM-2, and graph convolutional networks (GCN).
• Integration of deep learning-based activity prediction with Rosetta analysis, molecular docking, and molecular dynamics simulations.
• Screening of a targeted mutant library and wet-lab validation of promising candidates.

Main Results:

• EGCA-Net successfully identified four novel RhlA mutants with enhanced stability and substrate binding.
• The mutant R74A_L148C_S173A exhibited a 3.6-fold increase in enzymatic activity compared to wild-type, reaching 373.38 U/mg.
• Other identified mutants also showed significantly improved activity (290-317 U/mg).

Conclusions:

• The EGCA-Net framework enables rapid and efficient screening of enzyme mutants.
• Engineered RhlA variants demonstrate substantial improvements in catalytic potential.
• This study offers a powerful approach for accelerating enzyme engineering and biotechnological applications.