Evidence map›Paper›PMID 32567134›Full record

ArticleProtein science : a publication of the Protein Society2020

Harnessing synthetic biology to enhance heterologous protein expression.

Shlomo Zarzhitsky, Alex Jiang, Elizabeth E Stanley, Michael H Hecht

Abstract read
In one paragraph

Article in Protein science : a publication of the Protein Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Harnessing synthetic biology to enhance heterologous protein expression.Protein science : a publication of the Protein Society · 2020
    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.

Shlomo ZarzhitskyDepartment of Chemistry, Princeton University, Princeton, New Jersey, USA.ORCID 0000-0003-2635-5224
Alex JiangDepartment of Molecular Biology, Princeton University, Princeton, New Jersey, USA.
Elizabeth E StanleyDepartment of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, USA.
Michael H HechtDepartment of Chemistry, Princeton University, Princeton, New Jersey, USA.ORCID 0000-0002-5538-9813

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ability to express heterologous proteins in microbial hosts is crucial for many areas of research and technology. In most cases, however, successful expression and purification of the desired protein require fusion to another protein. To date, all fusion partners have been chosen from natural sequences, which evolved for other purposes, and may not be optimal fusion partners. However, the rise of synthetic biology and protein design make it possible to design and optimize fusion proteins using novel sequences that did not arise in nature. Here, we describe a series of De novo Expression Enhancer Proteins (DEEPs) that facilitate high-level expression and facile purification of heterologous proteins and peptides. To test the DEEP system, a de novo protein was fused to several target proteins covering a range of sizes and solubilities. In all cases, fusions to DEEP outperformed fusions to SUMO, a commonly used natural fusion partner. The availability of novel proteins that can be engineered for specific fusion applications could be beneficial to enhance the expression of a wide range of heterologous proteins.

Indexed as

Cloning, MolecularGene ExpressionProtein Processing, Post-TranslationalRecombinant Fusion ProteinsSynthetic BiologyEscherichia coliRecombinant Fusion Proteinsde novo protein designfusion tagprotein expressionpurification tag

Identifiers

PMID32567134
PMCPMC7380667

What OpenQuestion holds

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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.