Evidence map›Paper›PMID 34377363›Full record

ArticleComputational and structural biotechnology journal2021

Insight into membraneless organelles and their associated proteins: Drivers, Clients and Regulators.

Fernando Orti, Alvaro M Navarro, Andres Rabinovich, Shoshana J Wodak, Cristina Marino-Buslje

Open access · goldAbstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 9% 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.

  1. Pooled it
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  3. Review
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  5. Article
  6. Review
  7. Article
  8. Article
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  11. Article
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  13. International journal of molecular sciences · 2023
    Article
  14. Article
  15. Review
  16. Databases for intrinsically disordered proteins.Acta crystallographica. Section D, Structural biology · 2022
    Review
  17. Protein conformation and biomolecular condensates.Current research in structural biology · 2022
    Review
  18. Article
  19. 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 at 2 institutions in 2 countries.

Fernando OrtiBioinformatics Unit, Fundación Instituto Leloir. Avda. Patricias Argentinas 435, Buenos Aires B1405WE, Argentina.
Alvaro M NavarroBioinformatics Unit, Fundación Instituto Leloir. Avda. Patricias Argentinas 435, Buenos Aires B1405WE, Argentina.
Andres RabinovichBioinformatics Unit, Fundación Instituto Leloir. Avda. Patricias Argentinas 435, Buenos Aires B1405WE, Argentina.
Shoshana J WodakVIB-VUB Center for Structural Biology, Flemish Institute for Biotechnology, Brussels, Belgium.
Cristina Marino-BusljeBioinformatics Unit, Fundación Instituto Leloir. Avda. Patricias Argentinas 435, Buenos Aires B1405WE, Argentina.
Fundación Instituto Leloir · ARVrije Universiteit Brussel · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, attention has been devoted to proteins forming immiscible liquid phases within the liquid intracellular medium, commonly referred to as membraneless organelles (MLO). These organelles enable the spatiotemporal associations of cellular components that exchange dynamically with the cellular milieu. The dysregulation of these liquid-liquid phase separation processes (LLPS) may cause various diseases including neurodegenerative pathologies and cancer, among others. Until very recently, databases containing information on proteins forming MLOs, as well as tools and resources facilitating their analysis, were missing. This has recently changed with the publication of 4 databases that focus on different types of experiments, sets of proteins, inclusion criteria, and levels of annotation or curation. In this study we integrate and analyze the information across these databases, complement their records, and produce a consolidated set of proteins that enables the investigation of the LLPS phenomenon. To gain insight into the features that characterize different types of MLOs and the roles of their associated proteins, they were grouped into categories: High Confidence MLO associated (including Drivers and reviewed proteins), Potential Clients and Regulators, according to their annotated functions. We show that none of the databases taken alone covers the data sufficiently to enable meaningful analysis, validating our integration effort as essential for gaining better understanding of phase separation and laying the foundations for the discovery of new proteins potentially involved in this important cellular process. Lastly, we developed a server, enabling customized selections of different sets of proteins based on MLO location, database, disorder content, among other attributes (https://forti.shinyapps.io/mlos/).

Indexed as

ClientsLiquid condensatesLiquid-liquid phase separationLow-complexity regionsMembraneless organellesProtein disorderRegulatorsScaffold

Identifiers

PMID34377363
PMCPMC8318826
OpenAlexW3173986034

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.