Promiscuity-based enzyme selection for rational directed evolution experiments.

Area of Science:

• Protein engineering and computational biology.
• Enzyme discovery and directed evolution.
• Bioinformatics and computational biophysics.

Background:

• Protein engineers mimic natural evolution using error-prone PCR, DNA shuffling, and mutagenesis.
• Accelerating the discovery of new enzymes requires rational design strategies.
• Previous computational tools include CLASP for active site detection and PROMISE for promiscuous activity quantification.

Purpose of the Study:

• To describe a rational design flow (DECAAF) for identifying proteins amenable to minimal mutations for desired enzymatic functions.
• To provide user control for modeling catalytic site diversity and guiding the protein selection process.
• To demonstrate the DECAAF methodology in a practical application for plant-based antimicrobial enzyme development.

Main Methods:

• Utilizing the PROMISE methodology as the foundation for the DECAAF rational design flow.
• Implementing computational modeling to assess catalytic site diversity and guide protein selection.
• Applying the DECAAF flow to identify a plant protein scaffold for substitution of human neutrophil elastase.

Main Results:

• The DECAAF methodology enables the selection of proteins suitable for targeted engineering.
• The system allows for user-guided exploration of catalytic site variations.
• A successful application identified a plant protein for a chimeric antimicrobial enzyme.

Conclusions:

• DECAAF offers a rational approach to accelerate protein engineering and enzyme discovery.
• The methodology facilitates the creation of novel enzymes with specific functions.
• This work contributes to developing plant-based innate immune defense systems.