Evidence map›Paper›PMID 36334049›Full record

ArticleProtein science : a publication of the Protein Society2022

Rapid prediction and analysis of protein intrinsic disorder.

Guy W Dayhoff, Vladimir N Uversky

Abstract read
In one paragraph

Article in Protein science : a publication of the Protein Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

63 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. USP7 at PML Nuclear Bodies: A Protein Interaction Network Perspective.International journal of molecular sciences · 2026
    Article
  5. Stabilization of cyclin D3 protein by CDKN1A (p21Cell communication and signaling : CCS · 2026
    Article
  6. Article
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  9. Article
  10. Comparative assessment of binding residue predictions in intrinsically disordered regions.Protein science : a publication of the Protein Society · 2025
    Article
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  16. Review
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3 more citing papers are in PubMed but not listed here.

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

2 authors.

Guy W DayhoffDepartment of Chemistry, University of South Florida, Tampa, Florida, USA.
Vladimir N UverskyDepartment of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, University of South Florida, Tampa, Florida, USA.ORCID 0000-0002-4037-5857

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein intrinsic disorder is found in all kingdoms of life and is known to underpin numerous physiological and pathological processes. Computational methods play an important role in characterizing and identifying intrinsically disordered proteins and protein regions. Herein, we present a new high-efficiency web-based disorder predictor named Rapid Intrinsic Disorder Analysis Online (RIDAO) that is designed to facilitate the application of protein intrinsic disorder analysis in genome-scale structural bioinformatics and comparative genomics/proteomics. RIDAO integrates six established disorder predictors into a single, unified platform that reproduces the results of individual predictors with near-perfect fidelity. To demonstrate the potential applications, we construct a test set containing more than one million sequences from one hundred organisms comprising over 420 million residues. Using this test set, we compare the efficiency and accessibility (i.e., ease of use) of RIDAO to five well-known and popular disorder predictors, namely: AUCpreD, IUPred3, metapredict V2, flDPnn, and SPOT-Disorder2. We show that RIDAO yields per-residue predictions at a rate two to six orders of magnitude greater than the other predictors and completely processes the test set in under an hour. RIDAO can be accessed free of charge at https://ridao.app.

Indexed as

Computational BiologyIntrinsically Disordered ProteinsDatabases, ProteinProteomicsIntrinsically Disordered Proteinscomparative genomicscomparative proteomicsdisorder analysisdisorder predictionintrinsically disordered proteinsintrinsically disordered regionsprotein intrinsic disorderstructural bioinformatics

Identifiers

PMID36334049
PMCPMC9679974

What Socratic 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 Socratic graph.