Allosteric Constraints on Rewiring Inducible Repressors
Lewis, M.; Gupta, A.
Show abstract
Precise chemical control of transgene expression is central to synthetic biology, mammalian cell engineering, and gene therapy. Although tetracycline-responsive systems are widely used, converting an inducible repressor into a robust co-repressible regulator remains difficult. Systems engineered to activate DNA binding in response to ligand often exhibit elevated basal expression, weak switching, and limited dynamic range, suggesting that regulatory polarity is constrained by the underlying allosteric free-energy landscape. Here we combine thermodynamic modeling with matched mammalian reporter assays to examine the fundamental distinction between inducible and co-repressible regulation. Using a promoter-occupancy framework, we describe how ligand binding redistributes regulators between DNA-binding-competent and DNA-binding-incompetent conformations to control transcriptional output. Inducible repressors such as TetR activate transcription by reducing operator occupancy, whereas co-repressible systems must increase operator occupancy to suppress transcription, imposing fundamentally different energetic requirements. Experimental comparison of TetR-derived and PurR-derived regulators supports this thermodynamic interpretation. TetR-based systems produced strong ligand-dependent induction, whereas reverse TetR variants exhibited weaker co-repressible behavior and higher residual expression. In contrast, the natural co-repressible regulator PurR responded to hypoxanthine with ligand-stabilized DNA binding, and PurR-VP16 produced stronger ligand-dependent transcriptional activation than reverse TetR. Together, these results show that regulatory performance is determined by how efficiently ligand binding redistributes conformational states and suggest that natural co-repressible scaffolds may provide superior foundations for engineering ligand-activated transcriptional control.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Biphasic unbinding of Zur from DNA for transcription (de)repression in Live Bacteria 94%
- Cotranscriptional RNA strand exchange underlies the gene regulation mechanism in a purine-sensing transcriptional riboswitch 93%
- Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks 93%
Similar papers in this journal
- A parametrized two-domain thermodynamic model explains diverse mutational effects on protein allostery 95%
- Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins 93%
- Dissecting the sharp response of a canonical developmental enhancer reveals multiple sources of cooperativity 93%
Similar papers in this journal
- Systematic analysis of low-affinity transcription factor binding site clusters in vitro and in vivo establishes their functional relevance 93%
- Multiplexed characterization of rationally designed promoter architectures deconstructs combinatorial logic for IPTG-inducible systems 93%
- Epistasis shapes the fitness landscape of an allosteric specificity switch 93%
"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.