Evidence map›Paper›PMID 35795820›Full record

ArticleNature computational science2021

Physics-driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy.

Jerelle A Joseph, Aleks Reinhardt, Anne Aguirre, Pin Yu Chew, Kieran O Russell, Jorge R Espinosa, Adiran Garaizar, Rosana Collepardo-Guevara

Abstract read
In one paragraph

Article in Nature computational science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 217 papers.

0numbers the graph read from it
0cells of the map it votes in
217citing 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

217 citing papers in PubMed.

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  5. Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026
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  15. Article
  16. Structural Features of a Tiny Viral Protein, ORF7b of SARS-CoV-2.International journal of molecular sciences · 2026
    Article
  17. Fusion protein condensate formation via coiled-coil domains.Protein science : a publication of the Protein Society · 2026
    Article
  18. Article
  19. Article
  20. Distinguishing near- versus off-critical phase behaviors of intrinsically disordered proteins.Reports on progress in physics. Physical Society (Great Britain) · 2026
    Article

157 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

8 authors.

Jerelle A Joseph *Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
Aleks Reinhardt *Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
Anne AguirreYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
Pin Yu ChewYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
Kieran O RussellYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
Jorge R EspinosaDepartment of Physics, University of Cambridge, Cambridge, CB3 0HE, UK.
Adiran GaraizarDepartment of Physics, University of Cambridge, Cambridge, CB3 0HE, UK.
Rosana Collepardo-GuevaraYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.

Funding

European Research Council 803326
6 · The paper itself

Abstract

Various physics- and data-driven sequence-dependent protein coarse-grained models have been developed to study biomolecular phase separation and elucidate the dominant physicochemical driving forces. Here, we present Mpipi, a multiscale coarse-grained model that describes almost quantitatively the change in protein critical temperatures as a function of amino-acid sequence. The model is parameterised from both atomistic simulations and bioinformatics data and accounts for the dominant role of

Identifiers

PMID35795820
PMCPMC7612994

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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.