Evidence map›Paper›PMID 39029917›Full record

ArticleACS synthetic biology2024

Ligify: Automated Genome Mining for Ligand-Inducible Transcription Factors.

Simon d'Oelsnitz, Joshua D Love, Andrew D Ellington, David Ross

Abstract read
In one paragraph

Article in ACS synthetic biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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  5. Article
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  7. Article
  8. Review
  9. Synthetic biology (Oxford, England) · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Simon d'OelsnitzDepartment of Molecular Biosciences, University of Texas at Austin, Austin, Texas 78712, United States.ORCID 0000-0001-7512-9157
Joshua D LoveIndependent Web Developer, Bentonville, Arkansas 72712, United States.
Andrew D EllingtonDepartment of Molecular Biosciences, University of Texas at Austin, Austin, Texas 78712, United States.ORCID 0000-0001-6246-5338
David RossNational Institute of Standards and Technology, Gaithersburg, Maryland 20878, United States.ORCID 0000-0002-7790-218X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prokaryotic transcription factors can be repurposed into biosensors for the ligand-inducible control of gene expression, but the landscape of chemical ligands for which biosensors exist is extremely limited. To expand this landscape, we developed Ligify, a web application that leverages information in enzyme reaction databases to predict transcription factors that may be responsive to user-defined chemicals. Candidate transcription factors are then incorporated into automatically generated plasmid sequences that are designed to express GFP in response to the target chemical. Our benchmarking analyses demonstrated that Ligify correctly predicted 31/100 previously validated biosensors and highlighted strategies for further improvement. We then used Ligify to build a panel of genetic circuits that could induce a 47-fold, 5-fold, 9-fold, and 27-fold change in fluorescence in response to D-ribose, L-sorbose, isoeugenol, and 4-vinylphenol, respectively. Ligify should enhance the ability of researchers to quickly develop biosensors for an expanded range of chemicals and is publicly available at https://ligify.groov.bio.

Indexed as

Biosensing TechniquesTranscription FactorsEscherichia coliGreen Fluorescent ProteinsLigandsPlasmidsSoftwareGreen Fluorescent ProteinsLigandsTranscription Factorsbioinformaticsbiosensorgenome miningligandtranscription factorweb application

Identifiers

PMID39029917
PMCPMC11334909

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.