Mining the Metabolic Capacity of Clostridium sporogenes Aided by Machine Learning.
Mining the Metabolic Capacity of Clostridium sporogenes Aided by Machine Learning.
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机器学习辅助挖掘产孢梭菌的代谢能力。
DOI：
10.1002/anie.202319925
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Zhu,Xuejun
中科院分区：
文献类型：
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作者：
Ouyang,Huanrong;Xu,Zhao;Hong,Joshua;Malroy,Jeshua;Qian,Liangyu;Ji,Shuiwang;Zhu,Xuejun
厌氧微生物在胃肠道（GI）的微生物群中占主导地位，其中大部分小分子可以被降解或修饰。然而，肠道厌氧菌的巨大代谢能力与好氧细菌相比在很大程度上仍然是难以捉摸的，这主要是由于复杂的实验室环境的要求。在这项研究中，我们采用了一个计算机机器学习平台MoleculeX来预测肠道厌氧微生物生孢梭菌对小分子的代谢能力。实验表明，在预测为不稳定的前七名候选者中，有六个确实表现出C的不稳定性。产孢培养我们首次在细菌培养物中进一步鉴定了由依维莫司补充引起的几种代谢产物。利用生物信息学和体外生化分析，我们成功地确定了一个酶编码的基因组中的C。孢子发生负责依维莫司转化。因此，我们的框架可以促进未来对肠道小分子代谢的理解，通过个性化医疗进一步改善患者护理，并指导新的小分子药物和治疗方法的开发。
Anaerobes dominate the microbiota of the gastrointestinal (GI) tract, where a significant portion of small molecules can be degraded or modified. However, the enormous metabolic capacity of gut anaerobes remains largely elusive in contrast to aerobic bacteria, mainly due to the requirement of sophisticated laboratory settings. In this study, we employed an in silico machine learning platform, MoleculeX, to predict the metabolic capacity of a gut anaerobe,Clostridium sporogenes, against small molecules. Experiments revealed that among the top seven candidates predicted as unstable, six indeed exhibited instability inC. sporogenesculture. We further identified several metabolites resulting from the supplementation of everolimus in the bacterial culture for the first time. By utilizing bioinformatics and in vitro biochemical assays, we successfully identified an enzyme encoded in the genome ofC. sporogenesresponsible for everolimus transformation. Our framework thus can potentially facilitate future understanding of small molecules metabolism in the gut, further improve patient care through personalized medicine, and guide the development of new small molecule drugs and therapeutic approaches.