Biosensor libraries harness large classes of binding domains for construction of allosteric transcriptional regulators

Juárez, Javier F.; B, Lecube-Azpeitia; Brown, Stephen L.; Johnston, Christopher D; Church, George M.

doi:10.1038/s41467-018-05525-6

Cited by 60 publications

(40 citation statements)

References 68 publications

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“…For example, transcriptional regulators that respond to aromatic compounds are useful as biosensors [36]. In a recent study, high throughput methods were developed to combine protein domains for DNA binding and those for effector responses with the aim of creating benzoate-responsive biosensors [37]. This approach builds on the modular nature of bacterial transcriptional regulators.…”

Section: Discussionmentioning

confidence: 99%

Engineering CatM, a LysR-Type Transcriptional Regulator, to Respond Synergistically to Two Effectors

Tumen-Velasquez

Laniohan

Momany

et al. 2019

Genes

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The simultaneous response of one transcriptional regulator to different effectors remains largely unexplored. Nevertheless, such interactions can substantially impact gene expression by rapidly integrating cellular signals and by expanding the range of transcriptional responses. In this study, similarities between paralogs were exploited to engineer novel responses in CatM, a regulator that controls benzoate degradation in Acinetobacter baylyi ADP1. One goal was to improve understanding of how its paralog, BenM, activates transcription in response to two compounds (cis,cis-muconate and benzoate) at levels significantly greater than with either alone. Despite the overlapping functions of BenM and CatM, which regulate many of the same ben and cat genes, CatM normally responds only to cis,cis-muconate. Using domain swapping and site-directed amino acid replacements, CatM variants were generated and assessed for the ability to activate transcription. To create a variant that responds synergistically to both effectors required alteration of both the effector-binding region and the DNA-binding domain. These studies help define the interconnected roles of protein domains and extend understanding of LysR-type proteins, the largest family of transcriptional regulators in bacteria. Additionally, renewed interest in the modular functionality of transcription factors stems from their potential use as biosensors.

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Section: Discussionmentioning

confidence: 99%

Engineering CatM, a LysR-Type Transcriptional Regulator, to Respond Synergistically to Two Effectors

Tumen-Velasquez

Laniohan

Momany

et al. 2019

Genes

View full text Add to dashboard Cite

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“…Native promoters also exhibit context dependence, which complicates the porting of aTFs from non-model hosts (e.g., Pseudomonas or Acinetobacter) into common laboratory strains of E. coli. [15][16][17] Thus, repurposing the large diversity of known aTFs and the molecules they sense into useful diagnostic tools requires a better understanding of their fundamental mechanism of action.…”

Section: Introductionmentioning

confidence: 99%

Design of a transcriptional biosensor for the portable, on-demand detection of cyanuric acid

Li¹,

Silverman²,

Alam³

et al. 2019

Preprint

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Rapid molecular biosensing is an emerging application area for synthetic biology. Here, we engineer a portable biosensor for cyanuric acid (CYA), an analyte of interest for human and environmental health, using a LysR-type transcription regulator (LTTR) fromPseudomonas within the context of Escherichia coli gene expression machinery. To overcome cross-host portability challenges of LTTRs, we rationally engineered hybridPseudomonas-E. coli promoters by integrating DNA elements required for transcriptional activity and ligand-dependent regulation from both hosts, which enabled E. coli to function as whole-cell biosensor for CYA. To alleviate challenges of whole-cell biosensing, we adapted these promoter designs to function within a freeze-dried E. coli cell-free system to sense CYA. This portable, on-demand system robustly detects CYA within an hour from laboratory and real-world samples and works with both fluorescent and colorimetric reporters. This work elucidates general principles to facilitate the engineering of a wider array of LTTR-based environmental sensors.

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“…Structural approaches are limited by the relative scarcity of structural data ( Supplementary Fig. 1), computational tractability or difficulty with function-relevant spatio-temporal dynamics, which are particularly important for engineering [12][13][14] . Co-evolutionary methods operate poorly in underexplored regions of protein space (such as low-diversity viral proteins 15 ) and are not suitable for de novo designs.…”

mentioning

confidence: 99%

Unified rational protein engineering with sequence-based deep representation learning

Khimulya²,

et al. 2019

Self Cite

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Rational protein engineering requires a holistic understanding of protein function. Here, we apply deep learning to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily and biophysically grounded. We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space. Our data-driven approach predicts the stability of natural and de novo designed proteins, and the quantitative function of molecularly diverse mutants, competitively with the state-of-theart methods. UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task. UniRep is a versatile summary of fundamental protein features that can be applied across protein engineering informatics. Protein engineering has the potential to transform synthetic biology, medicine and nanotechnology. Traditional approaches to protein engineering rely on random variation and screening/selection without modeling the relationship between sequence and function 1,2. In contrast, rational engineering approaches seek to build quantitative models of protein properties, and use these models to more efficiently traverse the fitness landscape to Reprints and permissions information is available at www.nature.com/reprints.

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Biosensor libraries harness large classes of binding domains for construction of allosteric transcriptional regulators

Cited by 60 publications

References 68 publications

Engineering CatM, a LysR-Type Transcriptional Regulator, to Respond Synergistically to Two Effectors

Engineering CatM, a LysR-Type Transcriptional Regulator, to Respond Synergistically to Two Effectors

Design of a transcriptional biosensor for the portable, on-demand detection of cyanuric acid

Unified rational protein engineering with sequence-based deep representation learning

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