Hsp90 Functions in the Targeting and Outer Membrane Translocation Steps of Tom70-mediated Mitochondrial Import
Bibliographic record
Abstract
The Tom70 import receptor on the mitochondrial outer membrane specifically recognizes Hsp90 and Hsc70, a critical step for the import of mitochondrial preproteins, the targeting of which depends on these cytosolic chaperones. To analyze the role of Hsp90 in mitochondrial import, the effects of the Hsp90 inhibitors geldanamycin and novobiocin were compared. Geldanamycin occludes the N-terminal ATP-binding site of Hsp90, whereas novobiocin targets the C-terminal region of the chaperone. Here, novobiocin was found to inhibit preprotein import and, in particular, targeting to the purified cytosolic fragment of Tom70. Hsp90 cross-linking to preprotein and coprecipitation of Hsp90 with Tom70 were both impaired by novobiocin. Overall, novobiocin treatment increased preprotein aggregation, contributing to reduced import competence. In contrast, geldanamycin had no apparent effect on preprotein interactions with Hsp90, formation of preprotein-chaperone complexes, Hsp90 docking onto Tom70, or preprotein association with the outer membrane. Instead, geldanamycin impaired formation of preprotein import intermediates at the outer membrane. This suggests a novel active role for Hsp90 in import steps subsequent to Tom70 targeting. Our results outline the mechanisms of Hsp90 function in preprotein targeting and transport.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".