Stability of Secondary and Tertiary Structures of Virus-Like Particles Representing Noroviruses: Effects of pH, Ionic Strength, and Temperature and Implications for Adhesion to Surfaces
Bibliographic record
Abstract
Loss of ordered molecular structure in proteins is known to increase their adhesion to surfaces. The aim of this work was to study the stability of norovirus secondary and tertiary structures and its implications for viral adhesion to fresh foods and agrifood surfaces. The pH, ionic strength, and temperature conditions studied correspond to those prevalent in the principal vehicles of viral transmission (vomit and feces) and in the food processing and handling environment (pasteurization and refrigeration). The structures of virus-like particles representing GI.1, GII.4, and feline calicivirus (FCV) were studied using circular dichroism and intrinsic UV fluorescence. The particles were remarkably stable under most of the conditions. However, heating to 65°C caused losses of β-strand structure, notably in GI.1 and FCV, while at 75°C the α-helix content of GII.4 and FCV decreased and tertiary structures unfolded in all three cases. Combining temperature with pH or ionic strength caused variable losses of structure depending on the particle type. Regardless of pH, heating to pasteurization temperatures or higher would be required to increase GII.4 and FCV adhesion, while either low or high temperatures would favor GI.1 adhesion. Regardless of temperature, increased ionic strength would increase GII.4 adhesion but would decrease GI.1 adhesion. FCV adsorption would be greater at refrigeration, pasteurization, or high temperature combined with a low salt concentration or at a higher NaCl concentration regardless of temperature. Norovirus adhesion mediated by hydrophobic interaction may depend on hydrophobic residues normally exposed on the capsid surface at pH 3, pH 8, physiological ionic strength, and low temperature, while at pasteurization temperatures it may rely more on buried hydrophobic residues exposed upon structural rearrangement.
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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 | 0.001 |
| 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".