Evidence map›Paper›PMID 42681205›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Prediction of Intrinsically Disordered Regions That Bind RNA with DisoRDPbind.

Zhenling Peng, Lukasz Kurgan

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Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

2 authors.

Zhenling PengMOE Frontiers Science Center for Nonlinear Expectations, Shandong University, Qingdao, China.
Lukasz KurganDepartment of Computer Science, Virginia Commonwealth University, Richmond, VA, USA. lkurgan@vcu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A common cellular function of intrinsically disordered regions (IDRs) is molecular binding, which covers interactions with various partner molecules, including proteins, peptides, nucleic acids, lipids, and small molecules. While there are many computational predictors of binding IDRs, relatively few target IDRs that bind RNAs. We focus on one of the first tools designed to predict RNA-binding IDRs: DisoRDPbind. DisoRDPbind is available as an easy-to-use web server at http://biomine.cs.vcu.edu/servers/DisoRDPbind/ . We describe the predictive model employed by DisoRDPbind and discuss its predictive performance and runtime, which were recently measured in the second CAID (Critical Assessment of protein Intrinsic Disorder) experiment. We highlight the small computational footprint of DisoRDPbind, which allows for large/proteome-scale predictions. We detail how to use the web server interface of DisoRDPbind and provide an example to illustrate how to read and interpret its prediction results. While DisoRDPbind does not identify RNA chaperones directly, we present a case study of an RNA chaperone, HCV core protein, to illustrate the method's utility in studying RNA chaperones.

Indexed as

Computational BiologyIntrinsically Disordered ProteinsRNARNA-Binding ProteinsSoftwareProtein BindingIntrinsically Disordered ProteinsRNARNA-Binding ProteinsIntrinsically disordered regionsIntrinsic disorderMolecular recognitionProtein–RNA interactions

Identifiers

What OpenQuestion holds

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Registered trials

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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.