Evidence map›Paper›PMID 36607820›Full record

ArticleJournal of chemical theory and computation2023

Modeling Concentration-dependent Phase Separation Processes Involving Peptides and RNA via Residue-Based Coarse-Graining.

Gilberto Valdes-Garcia, Lim Heo, Lisa J Lapidus, Michael Feig

Abstract read
In one paragraph

Article in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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  5. Article
  6. Article
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  10. Accurate conformational ensembles of intrinsically disordered proteins using reweighting based on NMR chemical shifts.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  11. Article
  12. Article
  13. Driving forces of RNA condensation revealed through coarse-grained modeling with explicit MgProceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  14. Article
  15. All-atom simulations of biomolecular condensates.Current opinion in structural biology · 2025
    Review
  16. Generation of protein dynamics by machine learning.Current opinion in structural biology · 2025
    Review
  17. Article
  18. Prediction of phase-separation propensities of disordered proteins from sequence.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  19. Article
  20. 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

4 authors.

Gilberto Valdes-GarciaDepartment of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan48824, United States.
Lim HeoDepartment of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan48824, United States.ORCID 0000-0002-3153-2363
Lisa J LapidusDepartment of Physics and Astronomy, Michigan State University, East Lansing, Michigan48824, United States.ORCID 0000-0003-3136-2680
Michael FeigDepartment of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan48824, United States.ORCID 0000-0001-9380-6422

Funding

Modeling and dynamics of biomolecules on cellular scalesR35GM126948 · NIGMS · MICHIGAN STATE UNIVERSITY · PI Michael Feig · 2018 to 2026
$3.2M
NIGMS NIH HHS R35 GM126948
6 · The paper itself

Abstract

Biomolecular condensation, especially liquid-liquid phase separation, is an important physical process with relevance for a number of different aspects of biological functions. Key questions of what drives such condensation, especially in terms of molecular composition, can be addressed via computer simulations, but the development of computationally efficient yet physically realistic models has been challenging. Here, the coarse-grained model COCOMO is introduced that balances the polymer behavior of peptides and RNA chains with their propensity to phase separate as a function of composition and concentration. COCOMO is a residue-based model that combines bonded terms with short- and long-range terms, including a Debye-Hückel solvation term. The model is highly predictive of experimental data on phase-separating model systems. It is also computationally efficient and can reach the spatial and temporal scales on which biomolecular condensation is observed with moderate computational resources.

Identifiers

PMID36607820
PMCPMC10323037

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Registered trials

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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.