The expanding proteome of the molecular chaperone HSP90
Bibliographic record
Abstract
The molecular chaperone HSP90 maintains the activity and stability of a diverse set of "client" proteins that play key roles in normal and disease biology. Around 20 HSP90 inhibitors that deplete the oncogenic clientele have entered clinical trials for cancer. However, the full extent of the HSP90-dependent proteome, which encompasses not only clients but also proteins modulated by downstream transcriptional responses, is still incompletely characterized and poorly understood. Earlier large-scale efforts to define the HSP90 proteome have been valuable but are incomplete because of limited technical sensitivity. Here we discuss previous large-scale surveys of proteome perturbations induced by HSP90 inhibitors in light of a significant new study using state-of-the-art SILAC technology combined with more sensitive high-resolution mass spectrometry (MS) that extends the catalog of proteomic changes in inhibitor-treated cancer cells. Among wide-ranging changes, major functional responses include downregulation of protein kinase activity and the DNA damage response alongside upregulation of the protein degradation machinery. Despite this improved proteomic coverage, there was surprisingly little overlap with previous studies. This may be due in part to technical issues but is likely also due to the variability of the HSP90 proteome with the inhibitor conditions used, the cancer cell type and the genetic status of client proteins. We suggest future proteomic studies to address these factors, to help distinguish client protein components from indirect transcriptional components and to address other key questions in fundamental and translational HSP90 research. Such studies should also reveal new biomarkers for patient selection and novel targets for therapeutic intervention.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".