Effect of L-Arginine Modified Magnetic Nanoparticles (RMNPs) on Lysozyme in the Presence or Absence of Urea
Bibliographic record
Abstract
L-Arginine (Arg or R) is a non-toxigenic, metabolically versatile and conditionally essential amino acid. Single-pot synthesis of Arg modified magnetic nanoparticles (RMNPs) was achieved using magnetite nanoparticles (MNPs) and binary function of Arg as a functional group and an alkali precipitator. This modification is supposed to offer several advantages to the MNPs from a bio-application viewpoint. Here, the influence of RMNPs in the structure and function of the model protein hen egg white lysozyme (HEWL) is reported. After synthesis of RMNPs, VSM, XRD, FT-IR, Zetasizer, TEM, and SEM were used to characterize the nanoparticles. The impact of RMNPs was investigated on lysozyme structure and activity during storage and in the processes of denaturation and refolding by dilution or dialysis using fluorescence, circular dichroism (CD) and UV/Vis spectroscopies. RMNPs revealed structural ordering or disordering effects on lysozyme in a RMNP:HEWL ratio dependent manner. Accordingly, a concentration ratio of threshold (CRT) was determined at 0.296. At ratios lower than the CRT the protein gained more ordered structure with increased helicity. Inversely, HEWL was increasingly unfolded and helicity was decreased at ratios higher than the CRT, rendering the protein more disordered after interaction with RMNPs. At RMNPs:HEWL concentration ratios above and even below the CRT, 6 M urea had a further disordering effect. Nevertheless, significant improvements were observed in the refolding of the protein due to dilution or dialysis, by courtesy of the RMNPs. The presented data helps to expand the thriving applications of RMNPs in biotechnology and biomedicine.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".