The Real-Time Monitoring of the Thermal Unfolding of Tetramethylrhodamine-Labeled Actin
Bibliographic record
Abstract
Modification of actin at Cys (374) with tetramethylrhodamine maleimide (TMR-actin) has been used for visualization of actin filaments and to produce high-resolution crystal structures of actin. We show that TMR-actin exhibits a 21% decrease in absorbance at 557 nm upon thermal unfolding, likely due to the movement of TMR to a more hydrophobic environment upon rapid unfolding and protein aggregation. We took advantage of this property to test models of actin protein unfolding. A transition temperature ( T m) of 60.2 +/- 0.2 degrees C for Ca (2+).ATP.TMR-actin was determined using A 557 and agreed with our own determinations employing different techniques and previous work with unlabeled actin. Our data show that the dependence of TMR-actin thermal stability on the bound nucleotide and cations follows a trend of Ca (2+).ATP > Mg (2+).ATP > Ca (2+).ADP > Mg (2+).ADP. The activation energies and frequency factors for the thermal unfolding of TMR-actin determined with two methods were in good agreement with those previously determined for unlabeled actin. We observed a biphasic trend in the T m of TMR-actin with increasing nucleotide concentrations, supporting a two-pathway model for actin protein unfolding where one pathway dominates at different concentrations of nucleotide. Additionally, TMR-actin bound by DNase I or gelsolin segment-1 exhibited elevated transition temperatures.
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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 | 0.001 |
| 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".