FRET Assays for the Identification of C. albicans HSP90-Sba1 and Human HSP90α-p23 Binding Inhibitors
Bibliographic record
Abstract
Heat shock protein 90 (HSP90) is a critical target for anticancer and anti-fungal-infection therapies due to its central role as a molecular chaperone involved in protein folding and activation. In this study, we developed in vitro Förster Resonance Energy Transfer (FRET) assays to characterize the binding of C. albicans HSP90 to its co-chaperone Sba1, as well as that of the homologous human HSP90α to p23. The assay for human HSP90α binding to p23 enables selectivity assessment for compounds aimed to inhibit the binding of C. albicans HSP90 to Sba1 without affecting the physiological activity of human HSP90α. The combination of the two assays is important for antifungal drug development, while the assay for human HSP90α can potentially be used on its own for anticancer drug discovery. Since ATP binding of HSP90 is a prerequisite for HSP90-Sba1/p23 binding, ATP-competitive inhibitors can be identified with the assays. The specificity of binding of fusion protein constructs—HSP90-mNeonGreen (donor) and Sba1-mScarlet-I (acceptor)—to each other in our assay was confirmed via competitive inhibition by both non-labeled Sba1 and known ATP-competitive inhibitors. We utilized the developed assays to characterize the stability of both HSP90–Sba1 and HSP90α–p23 affinity complexes quantitatively. Kd values were determined and assessed for their precision and accuracy using the 95.5% confidence level. For HSP90-Sba1, the precision confidence interval (PCI) was found to be 70–120 (100 ± 20) nM while the accuracy confidence interval (ACI) was 100–130 nM. For HSP90α-p23, PCI was 180–260 (220 ± 40) nM and ACI was 200–270 nM. The developed assays were used to screen a nucleoside-mimetics library of 320 compounds for inhibitory activity against both C. albicans HSP90-Sba1 and human HSP90α-p23 binding. No novel active compounds were identified. Overall, the developed assays exhibited low data variability and robust signal separation, achieving Z factors > 0.5.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".