Genome mining as a tool for identifying novel glycopeptide antibiotics: insights from kineomicins

Glycopeptide antibiotics (GPAs) (vancomycin, teicoplanin, oritavancin, telavancin, dalbavancin) are clinically used to treat severe infections caused by multi-drug-resistant (MDR) Gram-positive pathogens and Clostridioides difficile. Most of these antibiotics are natural products or semisynthetic derivatives of natural products, blocking bacterial cell wall biosynthesis by binding to the terminal D-Ala-D-Ala terminus of the peptidoglycan precursor [1]. Recently, genome mining has emerged as a powerful approach for the identification of novel glycopeptide scaffolds and their previously unrecognized producing organisms [2]. In this framework, we screened 600 genomes belonging to the Pseudonocardiales order and identified 18 biosynthetic gene clusters (BGCs) predicted to encode previously unknown GPAs [3,4]. One of these molecules, named kineomicins from its producer strain Actinokineospora auranticolor, was produced up to an exceptionally high production rate, exceeding 1 g/L in a benchtop bioreactor. The resulting antibiotic complex was microbiologically characterized and the structure of its main congener, KmcB, was elucidated by LC-MS, MS/MS, and NMR spectroscopy, revealing a unique peptide scaffold [4]. Biological and chemical profiling of kineomicins is ongoing, highlighting the potential of this new antibiotic. Parallel studies on two additional putative GPA-producing strains are further validating genome mining as a key strategy in the discovery and development of new GPAs to counteract AMR spread.

[1] Marcone et al. 2018 Biotechnol Adv. 36(2):534-554. [2] Yushchuk et al. 2021. ACS Chem Biol. 16(5):915-928. [3] Andreo-Vidal et al. 2021. Antibiotics (Basel). 10(12):1533. [4] Yushchuk et al. 2025. Commun Chem. 8(1):134.