DeepEnzyme_Score: Identification of Global Mutations for modulating Enzyme functions

DeepEnzyme_Score is a composite deep-learning metric that captures both the thermodynamic and kinetic properties of enzymes. Integrated into an automated workflow that combine dynamic cross-correlation matrix (DCCM) analysis to identify remote residues dynamically coupled to catalytic-site perturbations, and MSA to construct focused mutant library, it prioritizes variants through rational screening for tailored enzyme function. This provides a generalizable platform to identify and evaluate global mutations sites, overcoming the limitations of traditional enzyme engineering strategies that primarily target residues near the active site.

Traditional enzyme engineering strategies primarily focus on residues around the active site. However, increasing evidence suggests that long-range dynamic coupling and allosteric communication play a critical role in enzyme catalysis.

We develop a workflow to: -Identify remote residues dynamically coupled to catalytic-site perturbations -Construct focused mutant library -Prioritize variants using DeepEnzyme_Score

This is developed by QUB Huang Group : https://www.huanggroup.co.uk/