
유전체 데이터를 활용하여 사이드로포어 합성 경로와 수용체를 정확하게 예측할 수 있는 생물정보학 파이프라인을 개발하였다. 이를 통해 기존에 알려진 것보다 훨씬 더 다양한 사이드로포어와 수용체 유전자를 발견하였다.


coremsg

Microbial secondary metabolites are a rich source for pharmaceutical discoveries and play crucial ecological functions. While tools exist to identify secondary metabolite clusters in genomes, precise sequence-to-function mapping remains challenging because neither function nor substrate specificity of synthesis enzymes can accurately be predicted. Here we developed a knowledge-guided bioinformatic pipeline to solve these issues. We analyzed 1928 genomes of Pseudomonas bacteria and focused on iron-scavenging pyoverdines as model metabolites. Our pipeline predicted 188 chemically different pyoverdines with nearly 100% structural accuracy and the presence of 94 distinct receptor groups required for the uptake of iron-loaded pyoverdines. Our pipeline unveils an enormous yet overlooked diversity of siderophores (151 new structures) and receptors (91 new groups). Our approach, combining feature sequence with phylogenetic approaches, is extendable to other metabolites and microbial genera, and thus emerges as powerful tool to reconstruct bacterial secondary metabolism pathways based on sequence data.

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### Competing Interest Statement

The authors have declared no competing interest.

 [1]: pending:yes

From sequence to molecules: Feature sequence-based genome mining uncovers the hidden diversity of bacterial siderophore pathways

다양한-박테리아-사이드로포어-경로의-숨겨진-다양성-발견-특징-서열-기반-유전체-마이닝


다양한 박테리아 사이드로포어 경로의 숨겨진 다양성 발견: 특징 서열 기반 유전체 마이닝
