Computational dual-loop frameworks bridging single-enzyme design and cascade tunnel network engineering for...

Area of Science:

• Computational Biology
• Biochemistry
• Enzyme Engineering

Background:

• Traditional enzyme engineering relies on empirical methods like directed evolution and rational design.
• These methods often struggle with system-level challenges such as enzyme stability and cofactor compatibility.
• Computational approaches offer a more predictive and model-driven strategy for enzyme optimization.

Purpose of the Study:

• To review recent advances in computational enzyme engineering.
• To integrate molecular-level enzyme design with pathway-level integration using a Design-Build-Test-Learn (DBTL) framework.
• To provide a structured overview of computational strategies for both single-enzyme and multi-enzyme systems.

Main Methods:

• Deep learning for structure and fitness prediction.
• Multiscale molecular simulations, including molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM).
• Analysis of multi-enzyme network engineering strategies like substrate tunnel engineering and electrostatic coupling.

Main Results:

• Computational methods enable predictive optimization of single enzymes by elucidating structure-function relationships.
• Advanced techniques facilitate the engineering of multi-enzyme systems for efficient intermediate transfer and balanced flux.
• Integration of computational tools within a dual-loop DBTL framework connects molecular design to pathway-level performance.

Conclusions:

• Computational biology is essential for advancing enzyme engineering beyond empirical methods.
• Integrating computational strategies across single-enzyme and multi-enzyme levels is crucial for overcoming system-level bottlenecks.
• Future directions involve further refining computational models and their integration for robust enzyme and pathway design.