Design of α-helical antimicrobial peptides with a high selectivity index

• Published in: Expert opinion on drug discovery Vol. 14; no. 10; p. 1053
• Main Authors: Juretić, Davor, Simunić, Juraj
• Format: Journal Article
• Language: English
• Published: England 03-10-2019
• Subjects: Anti-Bacterial Agents - chemistry; Anti-Bacterial Agents - pharmacology; Bacteria - drug effects; Drug Design; Drug Resistance, Multiple, Bacterial; Escherichia coli - drug effects; Humans; Molecular Weight; Peptides - chemistry; Peptides - pharmacology; Protein Conformation, alpha-Helical; Staphylococcus aureus - drug effects; Structure-Activity Relationship; Design; selectivity; prediction; antimicrobial peptides
• ISSN: 1746-045X
• DOI: 10.1080/17460441.2019.1642322

: Low-molecular-weight antibiotics are gradually rendered ineffective by multidrug-resistant bacteria. Promising replacements are fast-acting antimicrobial peptides, either found as host defense peptides or designed, but their main weakness in applications is low selectivity for bacterial cells.
: This paper explores how much human design has improved the evolutionary design for linear alpha-class antimicrobial peptides with a selective antibacterial activity. Activity data against
and
are collected from numerous publications reporting the hemolytic activity as well. Overall performance parameters are defined for easier ranking of best-performing peptides.
: Connecting structure to the specific activity of antimicrobial peptides should include considerations of which peptide features channel adaptable conformational changes toward pore-inducing interactions with anionic membranes. Imperfect amphipathicity, enhanced flexibility, self-assembly potential, and an oblique, only partially helical structure, can improve structure-activity and structure-selectivity relationships. The number of optimal combinations of antimicrobial activity and low toxicity are immense when dedicated databases are constructed, the best descriptors extracted and followed through model building, simulations, and selectivity predictions, with everything tightly connected to feedback cycles of
testing.