Evidence map›Paper›PMID 30299594›Full record

ReviewProteomics2018

Order in Disorder as Observed by the "Hydrophobic Cluster Analysis" of Protein Sequences.

Tristan Bitard-Feildel, Alexis Lamiable, Jean-Paul Mornon, Isabelle Callebaut

Abstract readReview
In one paragraph

Review in Proteomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Article
  2. Impact of GC content on de novo gene birth.Nature communications · 2026
    Article
  3. SHARK enables sensitive detection of evolutionary homologs and functional analogs in unalignable and disordered sequences.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Predicting Protein Conformational Disorder and Disordered Binding Sites.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  13. Deep learning in prediction of intrinsic disorder in proteins.Computational and structural biotechnology journal · 2022
    Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

4 authors.

Tristan Bitard-FeildelInstitut de Minéralogie, de Physique des Matériaux et de Cosmochimie (IMPMC), Institut de recherche pour le développement (IRD), UMR CNRS 7590, Muséum National d'Histoire Naturelle, Sorbonne Université, 75005, Paris, France.
Alexis LamiableInstitut de Minéralogie, de Physique des Matériaux et de Cosmochimie (IMPMC), Institut de recherche pour le développement (IRD), UMR CNRS 7590, Muséum National d'Histoire Naturelle, Sorbonne Université, 75005, Paris, France.
Jean-Paul MornonInstitut de Minéralogie, de Physique des Matériaux et de Cosmochimie (IMPMC), Institut de recherche pour le développement (IRD), UMR CNRS 7590, Muséum National d'Histoire Naturelle, Sorbonne Université, 75005, Paris, France.
Isabelle CallebautInstitut de Minéralogie, de Physique des Matériaux et de Cosmochimie (IMPMC), Institut de recherche pour le développement (IRD), UMR CNRS 7590, Muséum National d'Histoire Naturelle, Sorbonne Université, 75005, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hydrophobic cluster analysis (HCA) is an original approach for protein sequence analysis, which provides access to the foldable repertoire of the protein universe, including yet unannotated protein segments ("dark proteome"). Foldable segments correspond to ordered regions, as well as to intrinsically disordered regions (IDRs) undergoing disorder to order transitions. In this review, how HCA can be used to give insight into this last category of foldable segments is illustrated, with examples matching known 3D structures. After reviewing the HCA principles, examples of short foldable segments are given, which often contain short linear motifs, typically matching hydrophobic clusters. These segments become ordered upon contact with partners, with secondary structure preferences generally corresponding to those observed in the 3D structures within the complexes. Such small foldable segments are sometimes larger than the segments of known 3D structures, including flanking hydrophobic clusters that may be critical for interaction specificity or regulation, as well as intervening sequences allowing fuzziness. Cases of larger conditionally disordered domains are also presented, with lower density in hydrophobic clusters than well-folded globular domains or with exposed hydrophobic patches, which are stabilized by interaction with partners.

Indexed as

Cluster AnalysisHydrophobic and Hydrophilic InteractionsProtein Structure, SecondarySequence Analysis, Proteindark proteomedisorderfoldabilityHCAsecondary structure

Identifiers

PMID30299594
PMCPMC7168002

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.