A Boron-Dependent Antibiotic Derived from a Calcium-Dependent Antibiotic.

The prevalence of drug-resistant bacterial pathogens foreshadows a healthcare crisis. Calcium-dependent antibiotics (CDAs) are promising candidates to combat infectious diseases as many of them show modes of action (MOA) orthogonal to widespread resistance mechanisms. The calcium dependence is nonetheless one of the hurdles toward realizing their full potential. Using laspartomycin C (LspC) as a model, we explored the possibility of reducing, or even eliminating, its calcium dependence. We report herein a synthetic LspC analogue (B1) whose activity no longer depends on calcium and is instead induced by phenylboronic acid (PBA). In LspC, Asp1 and Asp7 coordinate to calcium to anchor it in the active conformation; these residues are replaced by serine in B1 and condense with PBA to form a boronic ester with the same anchoring effect. Using thin-layer chromatography, MS, NMR, and complementation assays, we demonstrate that B1 inhibits bacterial growth via the same MOA as LspC, i.e., sequestering the cell wall biosynthetic intermediate undecaprenyl phosphate. B1 is as potent and effective as LspC against several Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus. Our success in converting a CDA to a boron-dependent antibiotic opens a new avenue in the design and functional control of drug molecules.

Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides.

The traditional natural product discovery approach has accessed only a fraction of the chemical diversity in nature. The use of bioinformatic tools to interpret the instructions encoded in microbial biosynthetic genes has the potential to circumvent the existing methodological bottlenecks and greatly expand the scope of discovery. Structural prediction algorithms for nonribosomal peptides (NRPs), the largest family of microbial natural products, lie at the heart of this new approach. To understand the scope and limitation of the existing prediction algorithms, we evaluated their performances on NRP synthetase biosynthetic gene clusters. Our systematic analysis shows that the NRP biosynthetic landscape is uneven. Phenylglycine and its derivatives as a group of NRP building blocks (BBs), for example, have been oversampled, reflecting an extensive historical interest in the glycopeptide antibiotics family. In contrast, the benzoyl BB, including 2,3-dihydroxybenzoate (DHB), has been the most underexplored, hinting at the possibility of a reservoir of as yet unknown DHB containing NRPs with functional roles other than a siderophore. Our results also suggest that there is still vast unexplored biosynthetic diversity in nature, and the analysis presented herein shall help guide and strategize future natural product discovery campaigns. We also discuss possible ways bioinformaticians and biochemists could work together to improve the existing prediction algorithms.