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Record W6906270018 · doi:10.17169/refubium-46310

Measuring the Thermal Unfolding of Lysozyme: A Critical Comparison of Differential Scanning Fluorimetry and Differential Scanning Calorimetry

2025· article· en· W6906270018 on OpenAlexfundno aff

Bibliographic record

VenueRefubium (Universitätsbibliothek der Freien Universität Berlin) · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsDifferential scanning calorimetryEnthalpyHeat capacityFluorescence spectroscopyDynamic light scatteringCalorimetrySpectroscopyAnalytical Chemistry (journal)Atmospheric temperature range

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.261
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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