Light-directed evolution of dynamic, multi-state, and computational protein functionalities

Area of Science:

• Biochemistry
• Molecular Biology
• Synthetic Biology

Background:

• Engineering dynamic, multi-state protein functionalities is complex due to the need for selection across all protein states and transitions.
• Existing methods struggle to apply continuous selection pressure for evolving intricate protein behaviors.

Purpose of the Study:

• To develop a continuous directed evolution paradigm for proteins with dynamic and multi-state functionalities.
• To introduce optogenetic control for precise temporal regulation of protein activity during evolution.

Main Methods:

• Genetically engineered budding yeast with optogenetic inputs to control a protein of interest (POI).
• The POI's activity was linked to a cell-cycle-essential cyclin, creating dynamic selection pressure.
• The developed method, termed "optovolution," applies selection pressure on POI cycling at the timescale of minutes.

Main Results:

• Evolved 19 new variants of the LOV transcription factor El222, including green-light-responsive variants for LOV color-multiplexing.
• Discovered that YOR1 deletion eliminates the need for phycocyanobilin (PCB) supplementation when evolving the PhyB-Pif3 optogenetic system.
• Successfully evolved a non-light-responsive AND gate (PEST-rtTA), demonstrating method generality.

Conclusions:

• Optovolution provides a powerful platform for the continuous evolution of complex protein functionalities.
• The method facilitates the engineering of dynamic, multi-state, and computational protein behaviors previously challenging to evolve.
• Optovolution expands the toolkit for protein engineering and synthetic biology applications.