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.