Bioinformatic, structural, and biochemical analysis leads to the discovery of novel isonitrilases and decodes their substrate selectivity

Abstract

Bacterial species, such as Mycobacterium tuberculosis, utilize isonitrile-containing peptides (INPs) for trace metal trafficking, e.g., copper or zinc. Despite their importance, very few INP structures have been characterized to date. Reported INPs consist of a peptide backbone and β-isonitrile amide moieties. While the peptide backbone can be annotated using an adenylation domain predictor of non-ribosomal peptide synthetase (NRPS), determining the alkyl chain of β-isonitrile amide moieties remains challenging via conventional analytical techniques. In this study, we focus on non-heme iron and 2-oxoglutarate (Fe/2OG) dependent isonitrilases that exhibit inherent selectivity toward the alkyl chain length of the substrate, thus enabling the structural elucidation of INPs. Based on two known isonitrilase structures, we identified eight residue positions that control substrate selectivity. Using a custom Python program that we developed, BioSynthNexus, over 350 Fe/2OG isonitrilase genes were identified. One of these enzymes was engineered through mutations at eight selected positions, effectively modifying its substrate preference to favor either a shorter or a longer alkyl chain. Furthermore, by examining several annotated isonitrilases at eight selected positions, substrate preferences of several isonitrilases were predicted and validated through biochemical assays. Together, these findings allow for effective identification of isonitrilases and INPs, and establish a predictive framework for determining the preferred alkyl chain of β-isonitrile amide moieties.

Fig. 1. (A) Isonitrile-containing peptides (1–5) feature a peptide backbone and β-isonitrile amide moieties. Notably, variations exist in both the peptide backbones and alkyl chain lengths of the β-isonitrile amide moieties. (B) Highly analogous biosynthetic gene clusters imply that a similar strategy is deployed for INP biosynthesis. (C) The proposed biosynthetic pathway for INPs, illustrated using 1 as an example.

Fig. 2. SSN of 365 putative Fe/2OG isonitrilases. Nodes of the major genera are colored accordingly. The SSN is visualized with Cytoscape where edges indicate at least 60% identity.

Fig. 3. (A)–(C) Comparison of the pocket sizes for the alkyl chain in Fe/2OG isonitrilases. (A) Crystal structure of Rv0097 bound with heptyl-6 (n = 7, PDB ID: 8KHT). (B) Crystal structure of ScoE bound with methyl-6 (n = 1, PDB ID: 6L6X). (C) Predicted structure of Srug docked with heptyl-6 (n = 7). (D) and (E) Eight positions were selected based on structure-guided analysis. These selected residues are positioned around the alkyl chain pocket, illustrated using Rv0097 as an example (D). (F) General scheme for analyzing the isonitrile product. Substrate 6 with various alkyl chains (n = 1–11) was incubated with isonitrilases followed by derivatization using tetrazine to generate a common pyrazole product 8. (G) Substrate selectivity profiles of isonitrilases, where ScoE, Srug, and Rv0097 prefer short, medium, and long alkyl chain substrates, respectively. Protein structures were visualized in ChimeraX.

Fig. 4. Mutagenesis table of the eight selected positions and selectivity profiles of Srug variants. Several rounds of mutagenesis were employed to shift the substrate preference of Srug, enabling it to accommodate substrates with a shorter (A) or a longer (B) alkyl chain.

Fig. 5. (A) Eight selected positions of uncharacterized Fe/2OG isonitrilases found through in silico analysis. (B) Substrate selectivity profiles of selected isonitrilases.