Back

Engineering a light-responsive, orthogonal Lon protease in E. coli for targeted protein degradation

Coriano-Ortiz, C.;Fenton, L.;Kome, J.;Dunlop, M.

2026-06-19 Synthetic Biology
10.64898/2026.06.17.732960 bioRxiv
Show abstract

Optogenetic methods are powerful tools for synthetic biology, allowing light to control cellular processes. While most bacterial optogenetic systems regulate gene expression at the transcriptional level, relatively few enable post-translational control, which can provide faster and growth-independent regulation of protein activity. Here, we describe the development of a post-translational optogenetic tool in Escherichia coli using the Mesoplasma florum Lon (mfLon) protease, an AAA+ protease that is orthogonal to native E. coli degradation machinery. To engineer a light-responsive mfLon, we constructed a large library in which a blue-light responsive domain, Avena sativa LOV2, was introduced into nearly every codon position in the protease using an unbiased molecular approach. We screened 726 mfLon-LOV variants using fluorescence-activated cell sorting and multi-round enrichment campaigns. We identified a novel dark-active variant (mfLon-LOV-534) that degrades target proteins in the dark and is inactivated upon blue-light exposure. Characterization of this variant demonstrates that its proteolytic response can be tuned by varying blue-light intensity and transcriptional expression levels. Furthermore, we show that mfLon-LOV-534 can degrade a target protein in both exponential and stationary growth phases, which addresses the limitations of division-based protein dilution. This work establishes a scalable approach to engineering allosteric control in complex multimeric enzymes and provides a foundation for orthogonal, growth-independent control of protein stability in synthetic circuits.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.