Phage-Assisted Evolution of Allosteric Protein Switches

Nicholas T. Southern(Heidelberg University), Adrian Bachmann(Heidelberg University), Alisa Hovsepyan(Heidelberg University), Marielouise Griebl(Heidelberg University), Benedict Wolf(Heidelberg University), Nina Lemmen(Heidelberg University), Ann-Sophie Kroell(Heidelberg University), Simon Westermann(Heidelberg University), Jan Mathony(Heidelberg University), Dominik Niopek(Heidelberg University)
Nature Communications
June 12, 2025
Cited by 3Open Access
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Abstract

Allostery, the transmission of locally induced conformational changes to distant functional sites, is a key mechanism for protein regulation. Artificial allosteric effectors enable remote manipulation of cell function; their engineering, however, is hampered by our limited understanding of allosteric residue networks. Here, we introduce a phage-assisted evolution platform for in vivo optimization of allosteric proteins. It applies opposing selection pressures to enhance activity and switchability of phage-encoded effectors and leverages retron-based recombineering to broadly explore fitness landscapes, introducing point mutations, insertions, and deletions. Applying this framework to the transcription factor AraC yielded near-binary optogenetic switches, with light-controlled activity spanning ~1000-fold dynamic range. Long-read sequencing across selection cycles enabled high-resolution tracking of evolving variant pools, revealing adaptive trajectories and context-dependent residue interactions. Mechanistically, we find that linker mutations promoting α-helix extension at the sensor-effector junction enhance conformational coupling between LOV2 and AraC. These variants emerge consistently across independently evolved pools, underscoring their functional relevance. Together, we develop a framework for the directed evolution of programmable allosteric switches in vivo. By coupling dynamic selection with deep mutational scanning and temporal sequencing, it enables both functional optimization and mechanistic insight into allosteric networks.


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