Lipidation of Antimicrobial Peptides as a Design Strategy for Future Alternatives to Antibiotics
Bibliographic record
Abstract
Multi-drug-resistant bacteria are becoming more prevalent, and treating these bacteria is becoming a global concern. One alternative approach to combat bacterial resistance is to use antimicrobial (AMPs) or host-defense peptides (HDPs) because they possess broad-spectrum activity, function in a variety of ways, and lead to minimal resistance. However, the therapeutic efficacy of HDPs is limited by a number of factors, including systemic toxicity, rapid degradation, and low bioavailability. One approach to circumvent these issues is to use lipidation, i.e., the attachment of one or more fatty acid chains to the amine groups of the N-terminus or a lysine residue of an HDP. In this review, we examined lipidated analogs of 66 different HDPs reported in the literature to determine: (i) whether there is a link between acyl chain length and antibacterial activity; (ii) whether the charge and (iii) the hydrophobicity of the HDP play a role; and (iv) whether acyl chain length and toxicity are related. Overall, the analysis suggests that lipidated HDPs with improved activity over the nonlipidated counterpart had acyl chain lengths of 8-12 carbons. Moreover, active lipidated peptides attached to short HDPs tended to have longer acyl chain lengths. Neither the charge of the parent HDP nor the percent hydrophobicity of the peptide had an apparent significant impact on the antibacterial activity. Finally, the relationship between acyl chain length and toxicity was difficult to determine due to the fact that toxicity is quantified in different ways. The impact of these trends, as well as combined strategies such as the incorporation of d- and non-natural amino acids or alternative approaches, will be discussed in light of how lipidation may play a role in the future development of antimicrobial peptide-based alternatives to current therapeutics.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".