Designing and identifying β-hairpin peptide macrocycles with antibiotic potential

Abstract

Peptide macrocycles are a rapidly emerging class of therapeutic, yet the design of their structure and activity remains challenging. This is especially true for those with β-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 β-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 β-hairpin antimicrobial peptide activity.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 13, 2023
Source ID
10.1126/sciadv.ade0008

Entities

People

  • Bryan W Davies
  • Claus O. Wilke
  • Cory D DuPai
  • Despoina A.I. Mavridou
  • Gillian Davidson
  • Justin R. Randall
  • Kyra E. Groover
  • Sabrina L Slater
  • T. Jeffrey Cole

Organizations

  • University of Texas at Austin

Tags

Readers

  • Molecular and Cellular Biochemistry
  • Polymer Science and Technology

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Biotechnology