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Record W2995088666 · doi:10.1039/c9sc03191j

Interplay between intrinsically disordered proteins inside membraneless protein liquid droplets

2019· article· en· W2995088666 on OpenAlexaff
Yongsang Jo, Yongwon Jung

Bibliographic record

VenueChemical Science · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsKootenay Association for Science & Technology
FundersSamsung
KeywordsIntrinsically disordered proteinsChemistryChemical physicsNanotechnologyMaterials scienceBiophysicsBiologyBiochemistry

Abstract

fetched live from OpenAlex

Intrinsically disordered proteins (IDPs) in cells phase separate to form diverse membraneless organelles, which have condensed liquid droplet-like properties and often contain multiple IDPs. However, how potential interactions between different IDPs affect the dynamic behavior of these protein droplets is largely unknown. Here, we develop a rapid IDP clustering system to generate protein droplets with varied residue compositions and examine diverse interacting IDPs inside droplets. Three different IDP droplets actively recruited other diverse IDPs inside droplets with extremely varied enrichment (inside/outside) degrees (over 100-fold variation) under highly crowded conditions. The recruited IDPs were mostly mobile even inside highly immobile droplets. Among the five tested IDPs, the disordered region of Ddx4 helicase with its unique multiple charged residue blocks was noticeably influenced by droplet mobility. We also discovered that droplets of different IDPs could rapidly fuse to each other. Interestingly, some droplets were heterogeneously fused with segregated subcompartments, and this segregation was enhanced by droplet maturation and was more apparent for specific IDP pairs, in which the polar and charged residue compositions are highly different. The present study not only reports multiple peculiar behaviors of interacting IDP pairs inside droplets but also provides valuable information on generating membraneless organelle models with controllable droplet properties.

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 score0.002
Threshold uncertainty score0.490

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 science0.0010.001
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 head0.008
GPT teacher head0.280
Teacher spread0.272 · 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

Citations31
Published2019
Admission routes1
Has abstractyes

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