{"title":"SIDERITE: Unveiling hidden siderophore diversity in the chemical space through digital exploration","authors":"Ruolin He, Shaohua Gu, Jiazheng Xu, Xuejian Li, Haoran Chen, Zhengying Shao, Fanhao Wang, Jiqi Shao, Wen-Bing Yin, Long Qian, Zhong Wei, Zhiyuan Li","doi":"10.1002/imt2.192","DOIUrl":null,"url":null,"abstract":"<p>In this work, we introduced a siderophore information database (SIDERTE), a digitized siderophore information database containing 649 unique structures. Leveraging this digitalized data set, we gained a systematic overview of siderophores by their clustering patterns in the chemical space. Building upon this, we developed a functional group-based method for predicting new iron-binding molecules with experimental validation. Expanding our approach to the collection of open natural products (COCONUT) database, we predicted a staggering 3199 siderophore candidates, showcasing remarkable structure diversity that is largely unexplored. Our study provides a valuable resource for accelerating the discovery of novel iron-binding molecules and advancing our understanding of siderophores.\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":73342,"journal":{"name":"iMeta","volume":"3 2","pages":""},"PeriodicalIF":23.7000,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/imt2.192","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"iMeta","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/imt2.192","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
In this work, we introduced a siderophore information database (SIDERTE), a digitized siderophore information database containing 649 unique structures. Leveraging this digitalized data set, we gained a systematic overview of siderophores by their clustering patterns in the chemical space. Building upon this, we developed a functional group-based method for predicting new iron-binding molecules with experimental validation. Expanding our approach to the collection of open natural products (COCONUT) database, we predicted a staggering 3199 siderophore candidates, showcasing remarkable structure diversity that is largely unexplored. Our study provides a valuable resource for accelerating the discovery of novel iron-binding molecules and advancing our understanding of siderophores.