Evidence map›Paper›PMID 23414305›Full record

ReviewAnnual review of biochemistry2013

Signal recognition particle: an essential protein-targeting machine.

David Akopian, Kuang Shen, Xin Zhang, Shu-ou Shan

Open access · greenAbstract readReview
In one paragraph

Review in Annual review of biochemistry, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 290 papers.

0numbers the graph read from it
0cells of the map it votes in
290citing papers in PubMed
28.8field-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

290 citing papers in PubMed, 457 citations in OpenAlex.

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  16. Differential mRNA localization of karyopherin-β2 homologs inbioRxiv : the preprint server for biology · 2026
    Article
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230 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

4 authors at 1 institution in 1 country.

David AkopianDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA.
Kuang Shen
Xin Zhang
Shu-ou Shan
California Institute of Technology · US

Funding

PREDOCTORAL TRAINING IN BIOLOGY AND CHEMISTRYT32GM007616 · NIGMS · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI STERNBERG, PAUL WARREN · 1985 to 2019
$22.9M
Molecular Mechanism of Protein Targeting by the Signal Recognition ParticleR01GM078024 · NIGMS · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI SHAN, SHU-OU · 2007 to 2020
$4.4M
NIGMS NIH HHS 5T32GM07616NIGMS NIH HHS GM078024NIGMS NIH HHS R01 GM078024NIGMS NIH HHS T32 GM007616
6 · The paper itself

Abstract

The signal recognition particle (SRP) and its receptor compose a universally conserved and essential cellular machinery that couples the synthesis of nascent proteins to their proper membrane localization. The past decade has witnessed an explosion in in-depth mechanistic investigations of this targeting machine at increasingly higher resolutions. In this review, we summarize recent work that elucidates how the SRP and SRP receptor interact with the cargo protein and the target membrane, respectively, and how these interactions are coupled to a novel GTPase cycle in the SRP·SRP receptor complex to provide the driving force and enhance the fidelity of this fundamental cellular pathway. We also discuss emerging frontiers in which important questions remain to be addressed.

Indexed as

AnimalsBacterial ProteinsCell MembraneGTP PhosphohydrolasesHumansMammalsProtein BindingProtein TransportReceptors, Cytoplasmic and NuclearReceptors, PeptideSignal Recognition ParticleSignal TransductionBacterial ProteinsGTP PhosphohydrolasesReceptors, Cytoplasmic and NuclearReceptors, Peptidesignal peptide receptorSignal Recognition Particle

Identifiers

PMID23414305
PMCPMC3805129
OpenAlexW2140435480

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.