Real-Time Protein Unfolding: A Method for Determining the Kinetics of Native Protein Denaturation using a Quantitative Real-Time Thermocycler
Bibliographic record
Abstract
Protein stability can be monitored by many different techniques. However, these protocols are often lengthy, consume large amounts of protein, and require expensive and specialized instruments. Here we present a new protocol to analyze protein unfolding kinetics using a quantified real-time thermocycler. This technique enables the analysis of a wide range of denaturants (and their interactions with temperature change) on protein stability in a multi-well platform, where samples can be run in parallel under virtually identical conditions and with highly sensitive detection. Using this set-up, researchers can evaluate the half-maximal rate of protein denaturation (K(nd)), maximum rate of denaturation (D(max)), and the cooperativity of individual denaturants in protein unfolding (µ-coefficient). Both lysozyme and hexokinase are used as model proteins and urea as a model denaturant to illustrate this new method and the kinetics of protein unfolding that it provides. Overall, this method allows the researcher to explore a large number of denaturants, at either constant or variable temperatures, within the same assay, providing estimates of denaturation kinetics that have been previously inaccessible.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".