Evidence map›Paper›PMID 36833360›Full record

ReviewGenes2023

Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions.

Bingqing Han, Chongjiao Ren, Wenda Wang, Jiashan Li, Xinqi Gong

Abstract readReview
In one paragraph

Review in Genes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–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

9 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. Disordered Protein Tail Is Wagging Poly(ADP-ribosyl)ation.International journal of molecular sciences · 2025
    Article
  7. Review
  8. Review
  9. 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

5 authors.

Bingqing HanMathematical Intelligence Application Lab, Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China.
Chongjiao RenMathematical Intelligence Application Lab, Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China.
Wenda WangMathematical Intelligence Application Lab, Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China.
Jiashan LiMathematical Intelligence Application Lab, Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China.
Xinqi GongMathematical Intelligence Application Lab, Institute for Mathematical Sciences, Renmin University of China, Beijing 100872, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intrinsically Disordered Proteins (IDPs) and Regions (IDRs) exist widely. Although without well-defined structures, they participate in many important biological processes. In addition, they are also widely related to human diseases and have become potential targets in drug discovery. However, there is a big gap between the experimental annotations related to IDPs/IDRs and their actual number. In recent decades, the computational methods related to IDPs/IDRs have been developed vigorously, including predicting IDPs/IDRs, the binding modes of IDPs/IDRs, the binding sites of IDPs/IDRs, and the molecular functions of IDPs/IDRs according to different tasks. In view of the correlation between these predictors, we have reviewed these prediction methods uniformly for the first time, summarized their computational methods and predictive performance, and discussed some problems and perspectives.

Indexed as

Intrinsically Disordered ProteinsBinding SitesDrug DiscoveryHumansProtein DomainsIntrinsically Disordered Proteinscomputational predictionfunctionsinteractionsintrinsically disordered proteins and regions

Identifiers

PMID36833360
PMCPMC9956190

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.