Identification and engineering of highly functional potyviral proteases in cells using co-evolutionary models

Area of Science:

• Molecular Biology
• Virology
• Biochemistry

Background:

• Proteases within the Potyviridae family are crucial viral components, but their efficiency and substrate specificity remain incompletely understood.
• Comprehensive profiling of these proteases is essential for understanding viral replication and developing targeted antiviral strategies.

Purpose of the Study:

• To develop a predictive model for Potyviridae protease performance using co-evolutionary features.
• To experimentally validate the model's predictions and identify engineered proteases with enhanced activity.
• To demonstrate the model's utility by engineering protease crosstalk for selective synthetic cell-death induction.

Main Methods:

• Development of a computational model leveraging co-evolutionary features to predict protease performance.
• Single amino-acid resolution experimental validation of predicted protease activities.
• Engineering of novel proteases with improved catalytic efficiency and substrate specificity.
• Design and implementation of protease crosstalk for conditional activation of synthetic biological pathways.

Main Results:

• The developed model accurately predicts protease performance at single amino-acid resolution.
• Several engineered proteases demonstrated superior performance compared to the commercially available tobacco etch virus protease.
• Successful engineering of protease crosstalk enabled selective triggering of a synthetic cell-death program in human cells.

Conclusions:

• Co-evolutionary modeling provides a powerful approach for understanding and engineering viral protease function.
• The identified and engineered proteases offer potential tools for molecular biology and antiviral research.
• The demonstration of engineered protease crosstalk highlights the potential for precise control of cellular processes.