PLoS Computational Biology (May 2023)

Knowledge-guided data mining on the standardized architecture of NRPS: Subtypes, novel motifs, and sequence entanglements.

  • Ruolin He,
  • Jinyu Zhang,
  • Yuanzhe Shao,
  • Shaohua Gu,
  • Chen Song,
  • Long Qian,
  • Wen-Bing Yin,
  • Zhiyuan Li

DOI
https://doi.org/10.1371/journal.pcbi.1011100
Journal volume & issue
Vol. 19, no. 5
p. e1011100

Abstract

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Non-ribosomal peptide synthetase (NRPS) is a diverse family of biosynthetic enzymes for the assembly of bioactive peptides. Despite advances in microbial sequencing, the lack of a consistent standard for annotating NRPS domains and modules has made data-driven discoveries challenging. To address this, we introduced a standardized architecture for NRPS, by using known conserved motifs to partition typical domains. This motif-and-intermotif standardization allowed for systematic evaluations of sequence properties from a large number of NRPS pathways, resulting in the most comprehensive cross-kingdom C domain subtype classifications to date, as well as the discovery and experimental validation of novel conserved motifs with functional significance. Furthermore, our coevolution analysis revealed important barriers associated with re-engineering NRPSs and uncovered the entanglement between phylogeny and substrate specificity in NRPS sequences. Our findings provide a comprehensive and statistically insightful analysis of NRPS sequences, opening avenues for future data-driven discoveries.