Evidence map›Paper›PMID 23742071›Full record

ReviewThe FEBS journal2013

Engineering RNA-binding proteins for biology.

Yu Chen, Gabriele Varani

Open access · bronzeAbstract readReview
In one paragraph

Review in The FEBS journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed
4.2field-weighted citation impact, top 5% 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

63 citing papers in PubMed, 105 citations in OpenAlex.

  1. Article
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  3. Review
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  18. The mPlant biotechnology journal · 2022
    Article
  19. Article
  20. Article

3 more citing papers are in PubMed but not listed here.

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.

Yu ChenDepartment of Biochemistry, University of Washington, Seattle, WA 98195-1700, USA. chenyu@u.washington.edu
Gabriele Varani
University of Washington · US

Funding

Specificity of mRNA 3'-end processing and transcription terminationR01GM064440 · NIGMS · UNIVERSITY OF WASHINGTON · PI VARANI, GABRIELE · 2002 to 2015
$4.5M
Regulation of microRNA processingR01GM103834 · NIGMS · UNIVERSITY OF WASHINGTON · PI VARANI, GABRIELE · 2014 to 2017
$1.1M
NIGMS NIH HHS R01 GM064440NIGMS NIH HHS R01 GM103834
6 · The paper itself

Abstract

RNA-binding proteins play essential roles in the regulation of gene expression. Many have modular structures and combine relatively few common domains in various arrangements to recognize RNA sequences and/or structures. Recent progress in engineering the specificity of the PUF class RNA-binding proteins has shown that RNA-binding domains may be combined with various effector or functional domains to regulate the metabolism of targeted RNAs. Designer RNA-binding proteins with tailored sequence specificity will provide valuable tools for biochemical research as well as potential therapeutic applications. In this review, we discuss the suitability of various RNA-binding domains for engineering RNA-binding specificity, based on the structural basis for their recognition. We also compare various protein engineering and design methods applied to RNA-binding proteins, and discuss future applications of these proteins.

Indexed as

Models, MolecularProtein EngineeringAnimalsBiomedical ResearchDrug DesignHumansProtein Interaction Domains and MotifsRecombinant Fusion ProteinsRNA-Binding ProteinsZinc FingersRecombinant Fusion ProteinsRNA-Binding Proteinscomputational designin vitro evolutionphage displayprotein engineeringprotein-RNA interactionsRNA-binding domainsRNA-binding proteinsRNA recognition motifyeast three-hybrid systemzinc finger

Identifiers

PMID23742071
PMCPMC4052831
OpenAlexW1984189327

What OpenQuestion holds

Textmetadata
LicenceTDM
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.