Evidence map›Paper›PMID 32820806›Full record

ReviewBiochemical Society transactions2020

Zooming in on protein-RNA interactions: a multi-level workflow to identify interaction partners.

Alessio Colantoni, Jakob Rupert, Andrea Vandelli, Gian Gaetano Tartaglia, Elsa Zacco

Open access · hybridAbstract readReview
In one paragraph

Review in Biochemical Society transactions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

11 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Towards an IdealInternational journal of molecular sciences · 2022
    Review
  11. Review
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

5 authors at 3 institutions in 2 countries.

Alessio Colantoni *Center for Life Nanoscience, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy.
Jakob Rupert *Department of Biology 'Charles Darwin', Sapienza University of Rome, P.le A. Moro 5, Rome 00185, Italy.
Andrea Vandelli *Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain.
Gian Gaetano TartagliaCenter for Life Nanoscience, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy.
Elsa ZaccoCenter for Human Technologies, Istituto Italiano di Tecnologia, Via Erico Melen 83, 16152 Genoa, Italy.
Italian Institute of Technology · ITUniversitat Autònoma de Barcelona · ESUniversitat Pompeu Fabra · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interactions between proteins and RNA are at the base of numerous cellular regulatory and functional phenomena. The investigation of the biological relevance of non-coding RNAs has led to the identification of numerous novel RNA-binding proteins (RBPs). However, defining the RNA sequences and structures that are selectively recognised by an RBP remains challenging, since these interactions can be transient and highly dynamic, and may be mediated by unstructured regions in the protein, as in the case of many non-canonical RBPs. Numerous experimental and computational methodologies have been developed to predict, identify and verify the binding between a given RBP and potential RNA partners, but navigating across the vast ocean of data can be frustrating and misleading. In this mini-review, we propose a workflow for the identification of the RNA binding partners of putative, newly identified RBPs. The large pool of potential binders selected by in-cell experiments can be enriched by in silico tools such as catRAPID, which is able to predict the RNA sequences more likely to interact with specific RBP regions with high accuracy. The RNA candidates with the highest potential can then be analysed in vitro to determine the binding strength and to precisely identify the binding sites. The results thus obtained can furthermore validate the computational predictions, offering an all-round solution to the issue of finding the most likely RNA binding partners for a newly identified potential RBP.

Indexed as

Binding SitesComputer SimulationProtein BindingReproducibility of ResultsRNARNA-Binding ProteinsRNARNA-Binding Proteinsclipmolecular modellingprotein–RNA interaction predictionsprotein–RNA interactionsprotein–RNA interaction validation

Identifiers

PMID32820806
PMCPMC7458403
OpenAlexW3080365360

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.