Designing β-hairpin peptide macrocycles for antibiotic potential

Abstract

Peptide macrocycles are a rapidly emerging new class of therapeutic, yet the design of their structure and activity remains challenging. This is especially true for those with {beta}-hairpin structure due to weak folding properties and a propensity for aggregation. Here we use proteomic analysis and common antimicrobial features to design a large peptide library with macrocyclic {beta}-hairpin structure. Using an activity-driven high-throughput screen we identify dozens of peptides killing bacteria through selective membrane disruption and analyze their biochemical features via machine learning. Active peptides contain a unique constrained structure and are highly enriched for cationic charge with arginine in their turn region. Our results provide a synthetic strategy for structured macrocyclic peptide design and discovery, while also elucidating characteristics important for {beta}-hairpin antimicrobial peptide activity. Brief SummaryWe design, screen, and computationally analyze a synthetic macrocyclic {beta}-hairpin peptide library for antibiotic potential.