Measuring the Thermal Unfolding of Lysozyme: A Critical Comparison of Differential Scanning Fluorimetry and Differential Scanning Calorimetry
Bibliographic record
Abstract
The thermal unfolding of lysozyme in aqueous solution has been analyzed by (nano) differential scanning fluorimetry (nanoDSF) and differential scanning calorimetry (DSC). In addition, dynamic light scattering (DLS) acquired in parallel to the DSF measurements, was used to confirm that the change in hydrodynamic radius upon unfolding is rather small (RH,f=1.75 nm in the folded state; and RH,u=1.91 nm in the unfolded state). NanoDSF measurements were evaluated to characterize the folding/unfolding transition within the classical two-state folding model. The temperature of unfolding (Tm) is found to be the most robust quantity. The unfolding enthalpy DHu and the change of specific heat were also obtained and errors in the range of 5–10% and 30–50% were determined, respectively. A comparison of thermodynamic parameters from nanoDSF and DSC measurements provides evidence for an increasing unfolding enthalpy DHu with protein concentration. A comparison with data from literature suggests that a weak association in the folded state can lead to the observed change of the unfolding enthalpy. For Δcp significantly higher values is deduced from the analysis of temperature dependent nanoDSF measurements (10 kJ/(K mol)) as compare to DSC (3–5 kJ/(K mol)).
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".