Drugs targeting the particle for arrangement of quaternary structure (PAQosome) and protein complex assembly
Bibliographic record
Abstract
Introduction The PAQosome is a 12-subunit complex that acts as a co-factor of the molecular chaperones HSP90 and HSP70. This co-chaperone has been shown to participate in assembly and maturation of several protein complexes, including nuclear RNA polymerases, RNA processing factors, the ribosome, PIKKs, and others. Subunits of the PAQosome, adaptors, and clients have been reported to be involved in various diseases, making them interesting targets for drug discovery.Area covered In this review, the authors cover the detailed mechanisms of PAQosome and chaperone function. Specifically, the authors summarize the status of the PAQosome and some related chaperones and co-chaperones as candidate targets for drug discovery. Indeed, a number of compounds are currently being tested for the development of treatments against diseases, such as cancers and neurodegenerative conditions.Expert opinion Searching for new drugs targeting the PAQosome requires a better understanding of PAQosome subunit interactions and the discovery of new interaction partners. Thus, PAQosome subunit crystallization is an important experiment to initiate virtual screening against new target and the development of in silico tools such as AlphaFold-multimer could accelerate the search for new interaction partner and determine more rapidly the interaction pocket needed for virtual drug screening.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".