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 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".