Influence of Peptide Conjugation Sites on Lunatin–Alumina Nanoparticles: Implications for Membrane Interaction and Antimicrobial Activity
Bibliographic record
Abstract
Background/Objectives: The increasing prevalence of multidrug-resistant bacteria presents a major global health challenge, prompting a search for innovative antimicrobial strategies. This study aimed to develop and evaluate a novel nanobiostructure combining alumina nanoparticles (NPs) with the antimicrobial peptide lunatin-1 (Lun-1), forming peptide-functionalized nanofilaments. The main objective was to investigate how the site of peptide functionalization (C-terminal vs. N-terminal) affects membrane interactions and antibacterial activity. Methods: NP–peptide conjugates were synthesized via covalent bonding between lun-1 and alumina NP and characterized using transmission electron microscopy (TEM), X-ray diffraction (XRD), zeta potential analysis, dynamic light scattering (DLS), Fourier-transform infrared (FTIR), and solid-state 13C NMR. Antibacterial activities were assessed against different Gram-positive and Gram-negative strains. Biophysical analyses, including circular dichroism (CD), isothermal titration calorimetry (ITC), differential scanning calorimetry (DSC), and solid-state 2H NMR, were employed to evaluate peptide–membrane interactions in the presence of membrane-mimetic vesicles composed of POPC:POPG (3:1) and DMPC:DMPG (3:1). Results: Characterization confirmed the successful formation of NP–peptide nanofilaments. Functionalization at the N-terminal significantly influenced both antibacterial activity and peptide conformation compared to C-terminal attachment. Biophysical data demonstrated stronger membrane interaction and greater membrane disruption when lun-1 was conjugated at the N-terminal. Conclusions: The site of peptide conjugation plays a crucial role in modulating the biological and biophysical properties of NP–lunatin-1 conjugates. C-terminal attachment of lunatin-1 retains both membrane interaction and antibacterial efficacy, making it a promising strategy for the design of peptide-based nanotherapeutics targeting resistant pathogens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".