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The incorporation of the nonstandard amino acid para-nitro-L-phenylalanine (pN-Phe) within proteins has been used for diverse applications, including the termination of immune self-tolerance. However, the requirement for the provision of chemically synthesized pN-Phe to cells limits the contexts where this technology can be harnessed. Here we report the construction of a live bacterial producer of synthetic nitrated proteins by coupling metabolic engineering and genetic code expansion. We achieved the biosynthesis of pN-Phe in Escherichia coli by creating a pathway that features a previously uncharacterized nonheme diiron N-monooxygenase, which resulted in pN-Phe titers of 820 ± 130 µM after optimization. After we identified an orthogonal translation system that exhibited selectivity toward pN-Phe rather than a precursor metabolite, we constructed a single strain that incorporated biosynthesized pN-Phe within a specific site of a reporter protein. Overall, our study has created a foundational technology platform for distributed and autonomous production of nitrated proteins. 

Abstract With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.