Evidence map›Paper›PMID 30962376›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2019

Blind tests of RNA-protein binding affinity prediction.

Kalli Kappel, Inga Jarmoskaite, Pavanapuresan P Vaidyanathan, William J Greenleaf, Daniel Herschlag, Rhiju Das

Open access · bronzeAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.1field-weighted citation impact, top 23% 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

18 citing papers in PubMed, 27 citations in OpenAlex.

  1. Non-Enzymatic MGO-Glycation of SRSF2 Drives RNA Mis-Splicing.Journal of the American Chemical Society · 2026
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  14. Mapping invisible epitopes by NMR spectroscopy.The Journal of biological chemistry · 2020
    Article
  15. Article
  16. Review
  17. Article
  18. On the Upper Bounds of the Real-Valued Predictions.Bioinformatics and biology insights · 2019
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Kalli KappelBiophysics Program, Stanford University, Stanford, CA 94305.ORCID 0000-0002-2129-199X
Inga JarmoskaiteDepartment of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305.ORCID 0000-0001-5847-5867
Pavanapuresan P VaidyanathanDepartment of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305.
William J GreenleafDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305.ORCID 0000-0003-1409-3095
Daniel HerschlagDepartment of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305.ORCID 0000-0002-4685-1973
Rhiju DasBiophysics Program, Stanford University, Stanford, CA 94305; rhiju@stanford.edu.ORCID 0000-0001-7497-0972
Stanford University · US

Funding

Time-resolved small angle X-ray scatteringP01GM066275 · NIGMS · STANFORD UNIVERSITY · PI HERSCHLAG, DANIEL · 2003 to 2017
$17.8M
Internet-scale discovery of RNA bioengineering rulesR01GM100953 · NIGMS · STANFORD UNIVERSITY · PI DAS, RHIJU · 2012 to 2015
$1.8M
Quantitative high-throughput nucleic acid assays on a sequencing chipR01GM111990 · NIGMS · STANFORD UNIVERSITY · PI GREENLEAF, WILLIAM JAMES · 2014 to 2018
$1.5M
NIGMS NIH HHS P01 GM066275NIGMS NIH HHS R01 GM100953NIGMS NIH HHS R01 GM111990
6 · The paper itself

Abstract

Interactions between RNA and proteins are pervasive in biology, driving fundamental processes such as protein translation and participating in the regulation of gene expression. Modeling the energies of RNA-protein interactions is therefore critical for understanding and repurposing living systems but has been hindered by complexities unique to RNA-protein binding. Here, we bring together several advances to complete a calculation framework for RNA-protein binding affinities, including a unified free energy function for bound complexes, automated Rosetta modeling of mutations, and use of secondary structure-based energetic calculations to model unbound RNA states. The resulting Rosetta-Vienna RNP-ΔΔG method achieves root-mean-squared errors (RMSEs) of 1.3 kcal/mol on high-throughput MS2 coat protein-RNA measurements and 1.5 kcal/mol on an independent test set involving the signal recognition particle, human U1A, PUM1, and FOX-1. As a stringent test, the method achieves RMSE accuracy of 1.4 kcal/mol in blind predictions of hundreds of human PUM2-RNA relative binding affinities. Overall, these RMSE accuracies are significantly better than those attained by prior structure-based approaches applied to the same systems. Importantly, Rosetta-Vienna RNP-ΔΔG establishes a framework for further improvements in modeling RNA-protein binding that can be tested by prospective high-throughput measurements on new systems.

Indexed as

Protein BindingProtein ConformationRNARNA-Binding ProteinsBinding SitesHumansMutationThermodynamicsRNARNA-Binding Proteinsbinding affinityblind predictionconformational changeenergetic predictionRNA–protein complex

Identifiers

PMID30962376
PMCPMC6486753
OpenAlexW2938562744

What OpenQuestion holds

Textmetadata
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.