Single-molecule fluorescence study of the inhibition of the oncogenic functionality of STAT3
Bibliographic record
Abstract
Signal-Transducer-and-Activator-of-Transcription 3 (STAT3) protein plays an important role in the onset of cancers such as leukemia and lymphoma. In this study, we aim to test the effectiveness of a novel peptide drug designed to tether STAT3 to the phospholipid bilayer of the cell membrane and thus inhibit unwanted transcription. As a first step, STAT3 proteins were successfully labelled with tetramethylrhodamine (TMR), a fluorescent dye with suitable photostability for single molecule studies. The effectiveness of labelling was determined using fluorescence correlation spectroscopy in a custom built confocal microscope, from which diffusion times and hydrodynamic radii of individual proteins were determined. A newly developed fluorescein derivative label (F-NAc) has been designed to be incorporated into the structure of the peptide drug so that peptide-STAT3 interactions can be examined. This dye is spectrally characterized and is found to be well suited for its application to this project, as well as other single-molecule studies. The membrane localization via high-affinity cholesterol-bound small-molecule binding agents can be demonstrated by encapsulating TMR-labeled STAT3 and inhibitors within a vesicle model cell system. To this end, unilaminar lipid vesicles were examined for size and encapsulation ability. Preliminary results of the efficiency and stability of the STAT3 anchoring in lipid membranes obtained via quantitative confocal imaging and single-molecule spectroscopy using a custom-built multiparameter fluorescence microscope are reported here.
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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) | 0.001 | 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".