Evidence map›Paper›PMID 38453941›Full record

ArticleNature communications2024

Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme.

Simon d'Oelsnitz, Daniel J Diaz, Wantae Kim, Daniel J Acosta, Tyler L Dangerfield, Mason W Schechter, Matthew B Minus, James R Howard, Hannah Do, James M Loy and 3 more

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
11.5field-weighted citation impact, top 1% of its field
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

28 citing papers in PubMed, 46 citations in OpenAlex.

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  8. Programming Next-Generation Synthetic Biosensors by Genetic Circuit Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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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

13 authors at 3 institutions in 1 country.

Simon d'OelsnitzDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA. simonsnitz@gmail.com.ORCID 0000-0001-7512-9157
Daniel J DiazDepartment of Chemistry, University of Texas at Austin, Austin, TX, 78712, USA.ORCID 0000-0002-7891-2128
Wantae KimMcKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA.
Daniel J AcostaDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.ORCID 0000-0001-5950-536X
Tyler L DangerfieldDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.
Mason W SchechterDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.
Matthew B MinusDepartment of Chemistry, Prairie View A&M University, 100 University Dr, Prairie View, TX, 77446, USA.
James R HowardDepartment of Chemistry, University of Texas at Austin, Austin, TX, 78712, USA.
Hannah DoDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.
James M LoyDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.ORCID 0000-0001-8519-9482
Hal S AlperMcKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA.ORCID 0000-0002-8246-8605
Y Jessie ZhangDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.ORCID 0000-0002-9360-5388
Andrew D EllingtonDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.ORCID 0000-0001-6246-5338
The University of Texas at Austin · USCenter for Systems Biology · USPrairie View A&M University · US

Funding

Deciphering the phosphorylation pattern of RNA polymerase II for eukaryotic transcriptionR35GM148356 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI Yan Jessie Zhang · 2023 to 2026
$2.3M
Synthetic biology for controlled releaseR01EB026533 · NIBIB · UNIVERSITY OF TEXAS AT AUSTIN · PI ELLINGTON, ANDREW D · 2019 to 2022
$1.4M
NIBIB NIH HHS R01 EB026533NIGMS NIH HHS R35 GM148356
6 · The paper itself

Abstract

A major challenge to achieving industry-scale biomanufacturing of therapeutic alkaloids is the slow process of biocatalyst engineering. Amaryllidaceae alkaloids, such as the Alzheimer's medication galantamine, are complex plant secondary metabolites with recognized therapeutic value. Due to their difficult synthesis they are regularly sourced by extraction and purification from the low-yielding daffodil Narcissus pseudonarcissus. Here, we propose an efficient biosensor-machine learning technology stack for biocatalyst development, which we apply to engineer an Amaryllidaceae enzyme in Escherichia coli. Directed evolution is used to develop a highly sensitive (EC

Indexed as

AlkaloidsAmaryllidaceaeAmaryllidaceae AlkaloidsNarcissusHydrolasesMethyltransferasesPlantsAlkaloidsAmaryllidaceae AlkaloidsHydrolasesMethyltransferases

Identifiers

PMID38453941
PMCPMC10920890
OpenAlexW4392549818

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.