Molecular mechanisms of <scp>PI4K</scp> regulation and their involvement in viral replication
Bibliographic record
Abstract
Lipid phosphoinositides are master signaling molecules in eukaryotic cells and key markers of organelle identity. Because of these important roles, the kinases and phosphatases that generate phosphoinositides must be tightly regulated. Viruses can manipulate this regulation, with the Type III phosphatidylinositol 4-kinases (PI4KA and PI4KB) being hijacked by many RNA viruses to mediate their intracellular replication through the formation of phosphatidylinositol 4-phosphate (PI4P)-enriched replication organelles (ROs). Different viruses have evolved unique approaches toward activating PI4K enzymes to form ROs, through both direct binding of PI4Ks and modulation of PI4K accessory proteins. This review will focus on PI4KA and PI4KB and discuss their roles in signaling, functions in membrane trafficking and manipulation by viruses. Our focus will be the molecular basis for how PI4KA and PI4KB are activated by both protein-binding partners and post-translational modifications, with an emphasis on understanding the different molecular mechanisms viruses have evolved to usurp PI4Ks. We will also discuss the chemical tools available to study the role of PI4Ks in viral infection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".