Evidence map›Paper›PMID 23665222›Full record

ArticleCell reports2013

Discovering RNA-protein interactome by using chemical context profiling of the RNA-protein interface.

Marc Parisien, Xiaoyun Wang, George Perdrizet, Corissa Lamphear, Carol A Fierke, Ketan C Maheshwari, Michael J Wilde, Tobin R Sosnick, Tao Pan

Abstract read
In one paragraph

Article in Cell reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

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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

9 authors.

Marc ParisienDepartment of Biochemistry and Molecular Biology, University of Chicago, Chicago, IL 60637, USA.
Xiaoyun Wang
George Perdrizet
Corissa Lamphear
Carol A Fierke
Ketan C Maheshwari
Michael J Wilde
Tobin R Sosnick
Tao Pan

Funding

Beagle: A Supercomputer for Computational Biology, Simulation, and Data AnalysisS10RR029030 · NCRR · UNIVERSITY OF CHICAGO · PI FOSTER, IAN · 2010 to 2010
$7.0M
KINETIC/THERMODYNAMIC/STRUCTURAL STUDIES OF RNA FOLDINGR01GM057880 · NIGMS · UNIVERSITY OF CHICAGO · PI PAN, TAO · 1998 to 2011
$2.6M
Kinetic/Thermodynamic/Structural Studies of RNA FoldingR56GM057880 · NIGMS · UNIVERSITY OF CHICAGO · PI PAN, TAO · 2007 to 2007
$307k
NCRR NIH HHS S10 RR029030NCRR NIH HHS S10 RR029030-01NIGMS NIH HHS GM57880NIGMS NIH HHS R01 GM057880NIGMS NIH HHS R56 GM057880
6 · The paper itself

Abstract

RNA-protein (RNP) interactions generally are required for RNA function. At least 5% of human genes code for RNA-binding proteins. Whereas many approaches can identify the RNA partners for a specific protein, finding the protein partners for a specific RNA is difficult. We present a machine-learning method that scores a protein's binding potential for an RNA structure by utilizing the chemical context profiles of the interface from known RNP structures. Our approach is applicable even when only a single RNP structure is available. We examined 801 mammalian proteins and find that 37 (4.6%) potentially bind transfer RNA (tRNA). Most are enzymes involved in cellular processes unrelated to translation and were not known to interact with RNA. We experimentally tested six positive and three negative predictions for tRNA binding in vivo, and all nine predictions were correct. Our computational approach provides a powerful complement to experiments in discovering new RNPs.

Indexed as

Computational BiologyDatabases, ProteinHEK293 CellsHumansMolecular Docking SimulationNucleic Acid ConformationNucleotide MotifsProtein BindingRNA-Binding ProteinsRNA, TransferRNA-Binding ProteinsRNA, Transfer

Identifiers

PMID23665222
PMCPMC3769137

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.