Genome Mining and Discovery of Imiditides, a Family of RiPPs with a Class-Defining Aspartimide Modification.
J Am Chem Soc
; 145(34): 18834-18845, 2023 08 30.
Article
in En
| MEDLINE
| ID: mdl-37595015
ABSTRACT
Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a large and diverse class of natural products of ribosomal origin. In the past decade, various sophisticated machine-learning-based software packages have been established to discover novel RiPPs that do not resemble the known families. Here, we show that tailoring enzymes that cluster with various RiPP families can serve as effective bioinformatic seeds, providing a complementary approach for novel RiPP discovery. Leveraging the fact that O-methyltransferases homologous to protein isoaspartyl methyltransferases (PIMTs) are associated with lasso peptide, graspetide, and lanthipeptide biosynthetic gene clusters (BGCs), we utilized a C-terminal motif unique to RiPP-associated O-methyltransferases as the search query to discover a novel family of RiPPs, the imiditides. Our genome-mining algorithm reveals a total of 670 imiditide BGCs, distributed across Gram-positive bacterial genomes. In addition, we demonstrate the heterologous production of the founding member of the imiditide family, mNmaAM, encoded in the genome of Nonomuraea maritima. In contrast to other RiPP-associated PIMTs that recognize constrained peptides as substrates, the PIMT homologue in the mNmaAM BGC, NmaM, methylates a specific Asp residue on the linear precursor peptide, NmaA. The methyl ester is then turned into an aspartimide spontaneously. Substrate specificity is achieved by extensive charge-charge interactions between the precursor NmaA and the modifying enzyme NmaM suggested by both experiments and an AlphaFold model prediction. Our study shows that PIMT-mediated aspartimide formation is an emerging backbone modification strategy in the biosynthesis of multiple RiPP families.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Biological Products
/
Algorithms
Type of study:
Prognostic_studies
Language:
En
Journal:
J Am Chem Soc
Year:
2023
Document type:
Article
Affiliation country:
Estados Unidos