Evidence map›Paper›PMID 26776208›Full record

ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2016

A MOTIF-BASED METHOD FOR PREDICTING INTERFACIAL RESIDUES IN BOTH THE RNA AND PROTEIN COMPONENTS OF PROTEIN-RNA COMPLEXES.

Usha Muppirala, Benjamin A Lewis, Carla M Mann, Drena Dobbs

Open access · goldAbstract read
In one paragraph

Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 30% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Computational tools to study RNA-protein complexes.Frontiers in molecular biosciences · 2022
    Review
  2. Review
  3. Article
  4. Sequence-Based Prediction of RNA-Binding Residues in Proteins.Methods in molecular biology (Clifton, N.J.) · 2017
    Article
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 2 institutions in 1 country.

Usha Muppirala *Genome Informatics Facility, Iowa State University, Ames, Iowa, 50011, USA.
Benjamin A Lewis *Department of Computer Science, Truman State University, Kirksville, Missouri, 63501, USA, benlewis@truman.edu.
Carla M MannBioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, 50011, USA, cmmann@iastate.edu.
Drena DobbsDepartment of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa, 50011, USA, ddobbs@iastate.edu.
Iowa State University · USTruman State University · US

Funding

Discovery of Protein Seq.Struct.Func.RelationshipsR33GM066387 · NIGMS · IOWA STATE UNIVERSITY · PI HONAVAR, VASANT G. · 2005 to 2006
$545k
Protein Sequence Structure Function RelationshipsR21GM066387 · NIGMS · IOWA STATE UNIVERSITY · PI HONAVAR, VASANT G. · 2003 to 2004
$291k
NIGMS NIH HHS R01 GM066387NIGMS NIH HHS R21 GM066387NIGMS NIH HHS R33 GM066387
6 · The paper itself

Abstract

Efforts to predict interfacial residues in protein-RNA complexes have largely focused on predicting RNA-binding residues in proteins. Computational methods for predicting protein-binding residues in RNA sequences, however, are a problem that has received relatively little attention to date. Although the value of sequence motifs for classifying and annotating protein sequences is well established, sequence motifs have not been widely applied to predicting interfacial residues in macromolecular complexes. Here, we propose a novel sequence motif-based method for "partner-specific" interfacial residue prediction. Given a specific protein-RNA pair, the goal is to simultaneously predict RNA binding residues in the protein sequence and protein-binding residues in the RNA sequence. In 5-fold cross validation experiments, our method, PS-PRIP, achieved 92% Specificity and 61% Sensitivity, with a Matthews correlation coefficient (MCC) of 0.58 in predicting RNA-binding sites in proteins. The method achieved 69% Specificity and 75% Sensitivity, but with a low MCC of 0.13 in predicting protein binding sites in RNAs. Similar performance results were obtained when PS-PRIP was tested on two independent "blind" datasets of experimentally validated protein- RNA interactions, suggesting the method should be widely applicable and valuable for identifying potential interfacial residues in protein-RNA complexes for which structural information is not available. The PS-PRIP webserver and datasets are available at: http://pridb.gdcb.iastate.edu/PSPRIP/.

Indexed as

Amino Acid MotifsAmino Acid SequenceBase SequenceBinding SitesComputational BiologyDatabases, Nucleic AcidDatabases, ProteinEscherichia coli ProteinsModels, MolecularProtein BindingRibosomal ProteinsRNARNA, BacterialRNA-Binding ProteinsRNA, Ribosomal, 16SSoftwareEscherichia coli ProteinsRibosomal Proteinsribosomal protein S11ribosomal protein S4RNARNA, BacterialRNA-Binding ProteinsRNA, Ribosomal, 16S

Identifiers

PMID26776208
PMCPMC4721245
OpenAlexW2313051962

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.