SIDERITE:通过数字探索揭示化学空间中隐藏的苷元多样性

IF 23.7 Q1 MICROBIOLOGY iMeta Pub Date : 2024-04-05 DOI:10.1002/imt2.192
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
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引用次数: 0

摘要

在这项工作中,我们引入了一个苷元信息数据库(SIDERTE),这是一个包含 649 种独特结构的数字化苷元信息数据库。利用这一数字化数据集,我们通过苷元在化学空间中的聚类模式,系统地了解了苷元的概况。在此基础上,我们开发了一种基于官能团的方法,通过实验验证来预测新的铁结合分子。将我们的方法扩展到开放的天然产物(COCONUT)数据库,我们预测出了 3199 个惊人的嗜铁剂候选分子,展示了在很大程度上尚未开发的显著的结构多样性。我们的研究为加速发现新型铁结合分子和促进我们对络合铁的了解提供了宝贵的资源。
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SIDERITE: Unveiling hidden siderophore diversity in the chemical space through digital exploration

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.

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