Self-Assembly Pathways and Antimicrobial Properties of Lysozyme in Different Aggregation States
Bibliographic record
Abstract
Antimicrobial resistance in microorganisms will cause millions of deaths and pose a vast burden on health systems; therefore, alternatives to existing small-molecule antibiotics have to be developed. Lysozyme is an antimicrobial enzyme and has broad-spectrum antimicrobial activity in different aggregated forms. Here, we propose a reductive pathway to obtain colloidally stable amyloid-like worm-shaped lysozyme nanoparticles (worms) from hen egg white lysozyme (HEWL) and compare them to amyloid fibrils made in an acid hydrolysis pathway. The aggregation of HEWL into worms follows strongly pH-dependent kinetics and induces a structural transition from α-helices to β-sheets. Both HEWL worms and amyloid fibrils show broad-spectrum antimicrobial activity against the bacteria Staphylococcus aureus (Gram-positive), Escherichia coli (Gram-negative), and the fungus Candida albicans . The colloidal stability of the worms allows the determination of minimum inhibitory concentrations, which are lower than that for native HEWL in the case of S. aureus . Overall, amyloid fibrils have the strongest antimicrobial effect, likely due to the increased positive charge compared to native HEWL. The structural and functional characterizations of HEWL worms and amyloids investigated herein are critical for understanding the detailed mechanisms of antimicrobial activity and opens up new avenues for the design of broad-spectrum antimicrobial materials for use in various applications.
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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".