在机器学习的帮助下挖掘Clostridium sporogenes的代谢能力
Huanrong Ouyang1, Zhao Xu2, Joshua Hong1
1Department of Chemical Engineering, Texas A&M University, College Station, 77843, United States.
Angewandte Chemie (International ed. in English)
|January 29, 2024
概括
机器学习准确地预测了肠道无氧代谢. 研究人员发现了新的药物代谢物和酶,推进了个性化医学和药物开发.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 肠道微生物群无氧生物具有巨大的代谢能力,由于实验挑战,它们在很大程度上尚未被探索.
- 了解无氧代谢对于药物疗效和宿主微生物相互作用至关重要.
研究的目的:
- 使用in silico平台预测和验证肠道无氧菌Clostridium sporogenes对小分子的代谢活性.
- 为了确定新的代谢物和酶参与药物转化由肠道无气生物.
主要方法:
- 利用MoleculeX机器学习平台进行了代谢转换的in silico预测.
- 进行了与Clostridium sporogenes和小分子 (包括everolimus) 的体外实验.
- 采用生物信息学和生物化学测试来识别代谢物和酶.
主要成果:
- 在模型准确地预测了在C. sporogenes培养中七个候选小分子中的六个的不稳定性.
- 在C. sporogenes.中补充后确定了Everolimus的新代谢物.
- 在C. sporogenes中发现了一种特定的酶,该酶负责everolimus转化.
结论:
- 开发的计算框架有效地预测了肠道无氧代谢能力.
- 这种方法有助于理解小分子代谢,改进个性化医疗,指导药物开发.
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