Synthetic-Bioinformatic Natural Product Antibiotics with Diverse Modes of Action. Academic Article uri icon

Overview

abstract

  • Bacterial natural products have inspired the development of numerous antibiotics in use today. As resistance to existing antibiotics has become more prevalent, new antibiotic lead structures and activities are desperately needed. An increasing number of natural product biosynthetic gene clusters, to which no known molecules can be assigned, are found in genome and metagenome sequencing data. Here we access structural information encoded in this underexploited resource using a synthetic-bioinformatic natural product (syn-BNP) approach, which relies on bioinformatic algorithms followed by chemical synthesis to predict and then produce small molecules inspired by biosynthetic gene clusters. In total, 157 syn-BNP cyclic peptides inspired by 96 nonribosomal peptide synthetase gene clusters were synthesized and screened for antibacterial activity. This yielded nine antibiotics with activities against ESKAPE pathogens as well as Mycobacterium tuberculosis. Not only are antibiotic-resistant pathogens susceptible to many of these syn-BNP antibiotics, but they were also unable to develop resistance to these antibiotics in laboratory experiments. Characterized modes of action for these antibiotics include cell lysis, membrane depolarization, inhibition of cell wall biosynthesis, and ClpP protease dysregulation. Increasingly refined syn-BNP-based explorations of biosynthetic gene clusters should allow for more rapid identification of evolutionarily inspired bioactive small molecules, in particular antibiotics with diverse mechanism of actions that could help confront the imminent crisis of antimicrobial resistance.

publication date

  • August 11, 2020

Research

keywords

  • Anti-Bacterial Agents
  • Biological Products
  • Computational Biology
  • Mycobacterium tuberculosis

Identity

PubMed Central ID

  • PMC8011376

Scopus Document Identifier

  • 85089712441

Digital Object Identifier (DOI)

  • 10.1021/jacs.0c04376

PubMed ID

  • 32697091

Additional Document Info

volume

  • 142

issue

  • 33