Evidence map›Paper›PMID 38914665›Full record

ArticleScientific reports2024

Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline.

Brenna Norton-Baker, Mackenzie C R Denton, Natasha P Murphy, Benjamin Fram, Samuel Lim, Erika Erickson, Nicholas P Gauthier, Gregg T Beckham

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. 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

8 authors.

Brenna Norton-BakerRenewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA.
Mackenzie C R DentonRenewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA.
Natasha P MurphyRenewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA.
Benjamin FramDepartment of Systems Biology, Harvard Medical School, Boston, MA, USA.
Samuel LimDepartment of Systems Biology, Harvard Medical School, Boston, MA, USA.
Erika EricksonRenewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA.
Nicholas P GauthierDepartment of Systems Biology, Harvard Medical School, Boston, MA, USA. nicholas.gauthier.research@gmail.com.
Gregg T BeckhamRenewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA. gregg.beckham@nrel.gov.

Funding

Advanced Materials and Manufacturing Technologies Office (AMMTO) DE-AC36-08GO28308U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Genomic Science Program DE-SC0022024
6 · The paper itself

Abstract

As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purification using small-scale expression in E. coli and an affordable liquid-handling robot. This low-cost platform enables the purification of 96 proteins in parallel with minimal waste and is scalable for processing hundreds of proteins weekly per user. We demonstrate the performance of this method with the expression and purification of the leading poly(ethylene terephthalate) hydrolases reported in the literature. Replicate experiments demonstrated reproducibility and enzyme purity and yields (up to 400 µg) sufficient for comprehensive analyses of both thermostability and activity, generating a standardized benchmark dataset for comparing these plastic-degrading enzymes. The cost-effectiveness and ease of implementation of this platform render it broadly applicable to diverse protein characterization challenges in the biological sciences.

Indexed as

Escherichia coliRoboticsHigh-Throughput Screening AssaysHydrolasesPolyethylene TerephthalatesProtein EngineeringReproducibility of ResultsHydrolasesPolyethylene Terephthalates

Identifiers

PMID38914665
PMCPMC11196671

What OpenQuestion holds

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Registered trials

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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.