Biology subjects

Johnston, C. W.

Publications and source records attributed to Johnston, C. W..

3 recordsLinked to original sources

bioRxiv2025

Cyclic lipopeptide natural products as taxa-specific antibacterial inhibitors of the lipid II flippase

Antimicrobial resistance (AMR) is an existential threat to modern healthcare; one fueled by selection pressure provided by the use of broad-spectrum antibiotics in medicine and agriculture. As these antibiotics rely on a small set of chemical scaffolds and affect an even smaller number of biological targets, emergent AMR genes can spread through microbiomes to simultaneously inactivate multiple classes and generations of drugs. Long-overlooked for their perceived clinical limitations, antibacterial natural products with taxa-specific activities now present an underexplored source of design principles for precision antibiotics that can selectively eliminate individual microbes and limit community-wide incentives for AMR. Here, we present our re-investigation of one such taxa-specific antibacterial natural product, imacidin, a forgotten inhibitor of cell wall biosynthesis. We show that imacidin is the first natural product inhibitor of the peptidoglycan lipid II flippase MurJ, representing a larger, nascent class of taxa-specific cyclic lipopeptides that offer new leads for precision antibiotics.

microbiology↗

bioRxiv2024

Language model-guided anticipation and discovery of unknown metabolites

Despite decades of study, large parts of the mammalian metabolome remain unexplored. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks within human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains a low-throughput endeavor. Biochemical large language models have transformed the interpretation of DNA, RNA, and protein sequences, but have not yet had a comparable impact on understanding small molecule metabolism. Here, we present an approach that leverages chemical language models to discover previously uncharacterized metabolites. We introduce DeepMet, a chemical language model that learns the latent biosynthetic logic embedded within the structures of known metabolites and exploits this understanding to anticipate the existence of as-of-yet undiscovered metabolites. Prospective chemical synthesis of metabolites predicted to exist by DeepMet directs their targeted discovery. Integrating DeepMet with tandem mass spectrometry (MS/MS) data enables automated metabolite discovery within complex tissues. We harness DeepMet to discover several dozen structurally diverse mammalian metabolites. Our work demonstrates the potential for language models to accelerate the mapping of the metabolome.

bioinformatics↗

bioRxiv2024

Discovery and description of gammanonin: a widely distributed natural product from Gammaproteobacteria

Antibiotics are essential for modern medicine, but their use drives the evolution of antimicrobial resistance (AMR) that limits the long-term efficacy of any one drug. To keep pace with AMR and preserve our ability to treat bacterial infections, it is essential that we identify antibiotics with new structures and targets that are not affected by clinical resistance. Historically, most developmental candidates for antibiotics have come from microbial natural products, as they feature chemical structures and biological activities that have been honed over millions of years of evolution. Unfortunately, as classical bioactivity screens for natural product discovery are blind to the pharmacological properties of their hits, they often identify molecules with functional groups that limit their utility as drugs. One prominent example is actinonin, an inhibitor of bacterial peptide deformylase (PDF) whose activity is dependent on a hydroxamate moiety associated with toxicity in vivo. The abundance of bacterial genomes now presents an opportunity for target-based natural product discovery, where biosynthetic pathways can be mined for molecules that possess desired activities but lack toxic moieties. Here, we use bioinformatics to lead a chemotype-sensitive, target-based search for natural product inhibitors of bacterial PDF that lacks the conserved and problematic metal chelating group. We describe the discovery, heterologous expression, biosynthesis, total synthesis, and activity of the molecule gammanonin: an apparent actinonin homologue from Gammaproteobacteria. Moving forward, we hope this chemotype and target-driven methodology will help to expedite the discovery of new leads for antibiotic development.

biochemistry↗