Evidence map›Paper›PMID 38540707›Full record

ArticleBiomolecules2024

Assessment of Disordered Linker Predictions in the CAID2 Experiment.

Kui Wang, Gang Hu, Zhonghua Wu, Vladimir N Uversky, Lukasz Kurgan

Open access · goldAbstract read
In one paragraph

Article in Biomolecules, 2024. 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
0.7field-weighted citation impact, top 32% 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, 3 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Evaluation of predictions of disordered binding regions in the CAID2 experiment.Computational and structural biotechnology journal · 2025
    Review
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

5 authors at 3 institutions in 3 countries.

Kui WangSchool of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin 300071, China.
Gang HuSchool of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin 300071, China.
Zhonghua WuSchool of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China.ORCID 0000-0002-4191-0241
Vladimir N UverskyDepartment of Molecular Medicine, USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33613, USA.ORCID 0000-0002-4037-5857
Lukasz KurganDepartment of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.ORCID 0000-0002-7749-0314
Nankai University · CNUniversity of South Florida · USVirginia Commonwealth University · US

Funding

National Natural Science Foundation of China 92370128National Science Foundation DBI2146027National Science Foundation IIS2125218
6 · The paper itself

Abstract

Disordered linkers (DLs) are intrinsically disordered regions that facilitate movement between adjacent functional regions/domains, contributing to many key cellular functions. The recently completed second Critical Assessments of protein Intrinsic Disorder prediction (CAID2) experiment evaluated DL predictions by considering a rather narrow scenario when predicting 40 proteins that are already known to have DLs. We expand this evaluation by using a much larger set of nearly 350 test proteins from CAID2 and by investigating three distinct scenarios: (1) prediction residues in DLs vs. in non-DL regions (typical use of DL predictors); (2) prediction of residues in DLs vs. other disordered residues (to evaluate whether predictors can differentiate residues in DLs from other types of intrinsically disordered residues); and (3) prediction of proteins harboring DLs. We find that several methods provide relatively accurate predictions of DLs in the first scenario. However, only one method, APOD, accurately identifies DLs among other types of disordered residues (scenario 2) and predicts proteins harboring DLs (scenario 3). We also find that APOD's predictive performance is modest, motivating further research into the development of new and more accurate DL predictors. We note that these efforts will benefit from a growing amount of training data and the availability of sophisticated deep network models and emphasize that future methods should provide accurate results across the three scenarios.

Indexed as

Computational BiologyIntrinsically Disordered ProteinsDatabases, ProteinProteinsIntrinsically Disordered ProteinsProteinsassessmentdisordered linkersintrinsic disorderpredictionprotein functionprotein structure

Identifiers

PMID38540707
PMCPMC10968229
OpenAlexW4392233889

What Socratic holds

Textmetadata
LicenceCC BY
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.