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Molecular mechanisms of condensate modulation from energy-dominance analysis

2025· article· en· W4411091284 on OpenAlexafffund
Daoyuan Qian, Hannes Ausserwӧger, William E. Arter, Rob Scrutton, Timothy J. Welsh, Tadas Kartanas, Niklas Ermann, Seema Qamar, Charlotte M. Fischer, Tomas Šneideris, Peter St George‐Hyslop, Rohit V. Pappu, Tuomas P. J. Knowles

Bibliographic record

VenuePhysical Review Applied · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNovo NordiskWellcome TrustConsortium canadien en neurodégénérescence associée au vieillissementCanadian Institutes of Health ResearchFrances and Augustus Newman FoundationAlzheimer SocietyEuropean Commission
KeywordsDominance (genetics)Modulation (music)Environmental sciencePhysicsBiologyGeneticsAcousticsGene

Abstract

fetched live from OpenAlex

Biomolecular condensates in cells underpin cellular organization but have also been implicated in disease progression. As a result, modulation of condensate formation is becoming a path of interest to ameliorate biological malfunction. However, identifying the mechanism of action of a modulator is particularly challenging. This is because condensates are typically highly multicomponent, rendering it difficult to delineate which molecular interactions a modulator is influencing to drive or oppose condensate formation. Here, we extend the theoretical framework of energy dominance to include modulation effects, allowing us to uncover mechanisms of action of small molecular modulators through measuring species energetics. Using this approach, we experimentally investigate the effect of the small molecule suramin on condensates formed by the RNA-binding protein G3BP1 and RNA. We show that suramin specifically disrupts G3BP1-RNA interactions, as independently confirmed though orthogonal binding assays. Together, this work paves the way for systematic studies of condensate modulators in the future.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.290
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2025
Admission routes2
Has abstractyes

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