In the omics era, confident high-throughput analytical tools are crucial for the efficient identification of metabolites. Here, we present DeepHalo, a deep learning-integrated and hierarchically optimized workflow designed for high-throughput exploration of halogenated metabolites from high-resolution mass spectrometry-based metabolomics. DeepHalo leverages deep learning models combined with a comprehensive scoring to enhance the reliability of halogen predictions. It integrates PyOpenMS for fast isotope pattern detection and incorporates a halogen-based dereplication algorithm with GNPS molecular networking to efficiently exploit and annotate halogenates from complex biological matrices. To validate its performance, DeepHalo was applied to explore halogenated metabolites from 1296 microbial culture crudes, leading to the discovery of six families of structurally diverse halogenated molecules. This included a new class of cyclic depsipeptides, aglomycins A‒E, featuring rare 3-chloroanthranilic acid and/or epoxyvaline blocks. Additionally, a plausible biosynthetic pathway of aglomycins was proposed through bioinformatics analyses and targeted gene knockout experiments. Bioassays revealed that aglomycin A exhibits synergistic antibacterial activity with linezolid against vancomycin-resistant Enterococcus faecium (VRE) both in vitro and in vivo. We envision that DeepHalo, a user-friendly standalone executable freely available at https://github.com/xieyying/deephalo/releases/tag/DeepHalo_V1.0.0, will become a powerful tool for accelerating the discovery of halogenated “dark matter”.

Halogenated natural products  /  Metabolomics  /  Deep learning  /  Halogenases  /  Antibacterial activity  /  Vancomycin-resistant Enterococcus faecium (VRE)  /  High-resolution mass spectrometry  /  Metabolites