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Discovery of Borosin Catalytic Strategies and Function through Bioinformatic Profiling.

Aileen R LeeRiley S CarterAman S ImaniShravan R DommarajuGraham A HudsonDouglas A MitchellMichael F Freeman
Published in: ACS chemical biology (2024)
Borosins are ribosomally synthesized and post-translationally modified peptides (RiPPs) containing backbone α- N -methylations. These modifications confer favorable pharmacokinetic properties including increased membrane permeability and resistance to proteolytic degradation. Previous studies have biochemically and bioinformatically explored several borosins, revealing (1) numerous domain architectures and (2) diverse core regions lacking conserved sequence elements. Due to these characteristics, large-scale computational identification of borosin biosynthetic genes remains challenging and often requires additional, time-intensive manual inspection. This work builds upon previous findings and updates the genome-mining tool RODEO to automatically evaluate borosin biosynthetic gene clusters (BGCs) and identify putative precursor peptides. Using the new RODEO module, we provide an updated analysis of borosin BGCs identified in the NCBI database. From our data set, we bioinformatically predict and experimentally characterize a new fused borosin domain architecture, in which the modified natural product core is encoded N-terminal to the methyltransferase domain. Additionally, we demonstrate that a borosin precursor peptide is a native substrate of shewasin A, a reported aspartyl peptidase with no previously identified substrates. Shewasin A requires post-translational modification of the leader peptide for proteolytic maturation, a feature not previously observed in RiPPs. Overall, this work provides a user-friendly and open-access tool for the analysis of borosin BGCs and we demonstrate its utility to uncover additional biosynthetic strategies within the borosin class of RiPPs.
Keyphrases
  • genome wide
  • amino acid
  • bioinformatics analysis
  • small molecule
  • minimally invasive
  • transcription factor
  • machine learning
  • endothelial cells
  • electronic health record
  • deep learning
  • big data
  • neural network
  • drug induced