Evidence map›Paper›PMID 31395671›Full record

ArticleRNA (New York, N.Y.)2019

A structure-based model for the prediction of protein-RNA binding affinity.

Chandran Nithin, Sunandan Mukherjee, Ranjit Prasad Bahadur

Open access · bronzeAbstract read
In one paragraph

Article in RNA (New York, N.Y.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.5field-weighted citation impact, top 35% 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

9 citing papers in PubMed, 18 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Chandran NithinComputational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India.ORCID 0000-0001-8212-6093
Sunandan MukherjeeComputational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India.ORCID 0000-0002-4361-0103
Ranjit Prasad BahadurComputational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India.ORCID 0000-0002-6705-1713
Indian Institute of Technology Kharagpur · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein-RNA recognition is highly affinity-driven and regulates a wide array of cellular functions. In this study, we have curated a binding affinity data set of 40 protein-RNA complexes, for which at least one unbound partner is available in the docking benchmark. The data set covers a wide affinity range of eight orders of magnitude as well as four different structural classes. On average, we find the complexes with single-stranded RNA have the highest affinity, whereas the complexes with the duplex RNA have the lowest. Nevertheless, free energy gain upon binding is the highest for the complexes with ribosomal proteins and the lowest for the complexes with tRNA with an average of -5.7 cal/mol/Å

Indexed as

Models, MolecularNucleic Acid ConformationProtein ConformationAlgorithmsBinding SitesModels, TheoreticalMutationProtein BindingReproducibility of ResultsRNARNA-Binding ProteinsRNA, TransferStructure-Activity RelationshipRNARNA-Binding ProteinsRNA, Transferbinding affinityconformation changedissociation constantprotein–RNA interactionregression model

Identifiers

PMID31395671
PMCPMC6859855
OpenAlexW2969023103

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.