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Record W4312199646 · doi:10.1016/j.celrep.2022.111968

Liquid-liquid phase separation of nucleocapsid proteins during SARS-CoV-2 and HIV-1 replication

2022· review· en· W4312199646 on OpenAlexafffund
Bao-An Chau, Venessa Chen, Alan Cochrane, Leslie J. Parent, Andrew J. Mouland

Bibliographic record

VenueCell Reports · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill UniversityUniversity of TorontoJewish General Hospital
FundersNational Institute on Drug AbuseInstitute of Infection and ImmunityCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsRetrovirusViral replicationVirologyCoronavirusReplication (statistics)BiologyPandemicVirusHuman immunodeficiency virus (HIV)Cell biologyComputational biologyCoronavirus disease 2019 (COVID-19)Medicine

Abstract

fetched live from OpenAlex

The leap of retroviruses and coronaviruses from animal hosts to humans has led to two ongoing pandemics and tens of millions of deaths worldwide. Retrovirus and coronavirus nucleocapsid proteins have been studied extensively as potential drug targets due to their central roles in virus replication, among which is their capacity to bind their respective genomic RNAs for packaging into nascent virions. This review focuses on fundamental studies of these nucleocapsid proteins and how their intrinsic abilities to condense through liquid-liquid phase separation (LLPS) contribute to viral replication. Therapeutic targeting of these condensates and methodological advances are also described to address future questions on how phase separation contributes to viral replication.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.616
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.041
GPT teacher head0.368
Teacher spread0.327 · 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
GenreReview

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

Citations57
Published2022
Admission routes2
Has abstractyes

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