Evidence map›Paper›PMID 25310879›Full record

ArticleACS chemical biology2014

Recoding aminoacyl-tRNA synthetases for synthetic biology by rational protein-RNA engineering.

Andrew Hadd, John J Perona

Abstract read
In one paragraph

Article in ACS chemical biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 33% 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

5 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Therapeutic promise of engineered nonsense suppressor tRNAs.Wiley interdisciplinary reviews. RNA · 2021
    Review
  3. Article
  4. Overcoming Challenges in Engineering the Genetic Code.Journal of molecular biology · 2016
    Review
  5. 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

2 authors at 1 institution in 1 country.

Andrew HaddDepartment of Biochemistry & Molecular Biology, Oregon Health & Sciences University , 3181 Southwest Sam Jackson Park Road, Portland, Oregon 97239, United States.
John J Perona
Oregon Health & Science University · US

Funding

Specificity in the Synthesis of Aminoacyl-tRNAR01GM063713 · NIGMS · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI PERONA, JOHN J. · 2001 to 2012
$3.2M
NIGMS NIH HHS GM63713NIGMS NIH HHS R01 GM063713
6 · The paper itself

Abstract

We have taken a rational approach to redesigning the amino acid binding and aminoacyl-tRNA pairing specificities of bacterial glutaminyl-tRNA synthetase. The four-stage engineering incorporates generalizable design principles and improves the pairing efficiency of noncognate glutamate with tRNA(Gln) by over 10(5)-fold compared to the wild-type enzyme. Better optimized designs of the protein-RNA complex include substantial reengineering of the globular core region of the tRNA, demonstrating a role for specific tRNA nucleotides in specifying the identity of the genetically encoded amino acid. Principles emerging from this engineering effort open new prospects for combining rational and genetic selection approaches to design novel aminoacyl-tRNA synthetases that ligate noncanonical amino acids onto tRNAs. This will facilitate reconstruction of the cellular translation apparatus for applications in synthetic biology.

Indexed as

Protein EngineeringAmino Acyl-tRNA SynthetasesBacterial ProteinsBase SequenceEscherichia coliGene ExpressionGlutamic AcidModels, MolecularMolecular Sequence DataNucleic Acid ConformationRecombinant ProteinsRNA, Transfer, Amino AcylSynthetic BiologyAmino Acyl-tRNA SynthetasesBacterial ProteinsGlutamic Acidglutaminyl-tRNA synthetaseRecombinant ProteinsRNA, Transfer, Amino Acyl

Identifiers

PMID25310879
PMCPMC4273986
OpenAlexW2015280724

What OpenQuestion holds

Textmetadata
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.