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Record W2141813600 · doi:10.1186/1742-4690-9-79

From promoting to inhibiting: diverse roles of helicases in HIV-1 Replication

2012· review· en· W2141813600 on OpenAlexafffund
René-Pierre Lorgeoux, Fei Guo, Chen Liang

Bibliographic record

VenueRetrovirology · 2012
Typereview
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health Research
KeywordsReplication (statistics)Human immunodeficiency virus (HIV)VirologyHelicaseMedicineViral replicationComputational biologyBioinformaticsBiologyVirusGeneticsRNAGene

Abstract

fetched live from OpenAlex

Helicases hydrolyze nucleotide triphosphates (NTPs) and use the energy to modify the structures of nucleic acids. They are key players in every cellular process involving RNA or DNA. Human immunodeficiency virus type 1 (HIV-1) does not encode a helicase, thus it has to exploit cellular helicases in order to efficiently replicate its RNA genome. Indeed, several helicases have been found to specifically associate with HIV-1 and promote viral replication. However, studies have also revealed a couple of helicases that inhibit HIV-1 replication; these findings suggest that HIV-1 can either benefit from the function of cellular helicases or become curtailed by these enzymes. In this review, we focus on what is known about how a specific helicase associates with HIV-1 and how a distinct step of HIV-1 replication is affected. Despite many helicases having demonstrated roles in HIV-1 replication and dozens of other helicase candidates awaiting to be tested, a deeper appreciation of their involvement in the HIV-1 life cycle is hindered by our limited knowledge at the enzymatic and molecular levels regarding how helicases shape the conformation and structure of viral RNA-protein complexes and how these conformational changes are translated into functional outcomes in the context of 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.997

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.004

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 head0.059
GPT teacher head0.340
Teacher spread0.281 · 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.

Study designNot applicable
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

Citations34
Published2012
Admission routes2
Has abstractyes

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