Renal protein reactivity and stability of antibiotic amphenicols: structure and affinity
Bibliographic record
Abstract
In the present work, the molecular recognition of the oldest active amphenicols by the most popular renal carrier, lysozyme, was deciphered by using fluorescence, circular dichroism (CD) and molecular modeling at the molecular scale. Steady state fluorescence data showed that the recognition of amphenicol by lysozyme yields a static type of fluorescence quenching. This corroborates time-resolved fluorescence results that lysozyme-amphenicol adduct formation has a moderate affinity of 10(4) M(-1), and the driving forces were found to be chiefly hydrogen bonds, hydrophobic interactions and π stacking. Far-UV CD spectra confirmed that the spatial structure of lysozyme was slightly changed with a distinct reduction of α-helices in the presence of amphenicol, suggesting partial destabilization of the protein. Furthermore, via the extrinsic 8-anilino-1-naphthalenesulfonic acid fluorescence spectral properties and molecular modeling, one could see that the amphenicol binding site was situated at the deep crevice on the protein surface, and the ligand was also near to several crucial amino acid residues, such as Trp-62, Trp-63 and Arg-73. Simultaneously, contrastive studies of protein-amphenicols revealed clearly that some substituting groups, e.g. nitryl in the molecular structure of ligands, may be vitally important for the recognition activity of amphenicols with lysozyme. Due to the connection of amphenicols with fatal detrimental effects and because lysozyme has been applied as a drug carrier for proximal tubular targeting, the discussion herein is necessary for rational antibiotic use, development of safe antibiotics and particularly a better appraisal of the risks associated with human exposure to toxic agrochemicals.
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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".