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通过集群预测旧和新代谢物的途径

Thiru Siddharth¹, Nathan E Lewis²

¹Department of Computer Science and Engineering, Indian Institute of Information Technology, Bhopal, MP 462003, India.

Journal of theoretical biology

|December 4, 2023

概括

预测新代谢物的代谢途径是具有挑战性的. 这项研究引入了基于结构的方法,使用代谢物特征和聚类,成功将92%的已知代谢物与其途径联系起来.

科学领域:

生物化学生物化学
代谢学代谢学代谢学
生物信息学是一种生物信息学.

背景情况:

代谢途径对生命至关重要,但很难识别它们的新代谢物.
目前,人类新陈代谢数据库 (HMDB) 中只有60%的代谢物被分配到已知的途径.
阐明新化合物的代谢途径是耗时且资源密集的.

研究的目的:

开发和验证一种基于代谢物结构预测代谢途径的计算方法.
提高代谢物的分配率到它们各自的途径.
促进发现和理解新的代谢功能.

主要方法:

从SMILES (简化分子输入线输入系统) 标注中提取201个结构特征.
从PubMed摘要和HMDB中识别新的代谢物.
将聚类算法应用于代谢物特征以确定相关性.
量化代谢物之间的相关性,将它们与途径联系起来.

主要成果:

开发的方法成功将92%的已知代谢物与其正确的代谢途径联系起来.
聚类算法有效地识别了代谢物结构及其功能之间的关系.
该方法在预测已知代谢物的途径方面表现出高准确度.

结论:

这种基于结构的计算方法为预测新发现代谢物的代谢途径提供了宝贵的工具.
这些发现通过解决途径注释的挑战,大大推动了代谢学领域的发展.
该方法有可能通过使代谢物更快地进行功能性表征来加速生物研究.

关键词:

在AdaBoostClassifier中使用了AdaBoost.在K模式集群中,可以使用K模式集群.在K-原型集群化中.代谢物的预测预测.路径预测路径预测

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