Influence of NSAID Flurbiprofen on Protein Helix‐helix Interactions
Bibliographic record
Abstract
The mechanism of action of non‐steroidal anti‐inflammatory drugs (NSAIDs) is not fully understood, however, there is now some reports signifying that they may inter chelate into the membrane consequently influencing integral membrane protein folding. Given the recent appreciation of the microbiome, we considered investigating the influence of NSAIDS on the folding of bacterial multidrug resistance proteins. In this work, the Bacterial Adenylate Cyclase Two‐hybrid System (BACTH) was used to study the protein‐protein interactions of EmrE, from Escherichia coli , which is an inner integral membrane protein belonging to the Small Multi‐drug Resistant family. The in vivo BACTH assay was used as a technique to develop an enhanced understanding of the helix interactions of this protein as it resides within the membrane. In particular, a glycine motif found on helix 4 has been observed to be critical for the oligomerization and functionality of EmrE, in agreement to the known importance of these motifs in membrane proteins. In recent years it has been revealed that certain NSAIDs have the ability to bind to glycine motifs and enhance or disturb transmembrane segment interactions. Flurbiporfen, a common NSAID, was used in this work to explore if this drug would affect EmrE. Upon increasing concentrations of flurbiprofen it was found that the interactions between certain integral helix pairs were strengthened, whereas the interaction between the leucine zipper peptides, which was used as a positive for this work, demonstrated a decrease in helix interaction. Our work suggests that these drugs could influence bacterial protein structure and function.
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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) | 0.001 | 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".