Metagenomics and Systems Biology Laboratory, Indian Institute of Science Education and Research, Bhopal, Madhya Pradesh, India.
Sci Rep. 2017 Aug 29;7(1):9751. doi: 10.1038/s41598-017-10203-6.
The human gut microbiota is constituted of a diverse group of microbial species harbouring an enormous metabolic potential, which can alter the metabolism of orally administered drugs leading to individual/population-specific differences in drug responses. Considering the large heterogeneous pool of human gut bacteria and their metabolic enzymes, investigation of species-specific contribution to xenobiotic/drug metabolism by experimental studies is a challenging task. Therefore, we have developed a novel computational approach to predict the metabolic enzymes and gut bacterial species, which can potentially carry out the biotransformation of a xenobiotic/drug molecule. A substrate database was constructed for metabolic enzymes from 491 available human gut bacteria. The structural properties (fingerprints) from these substrates were extracted and used for the development of random forest models, which displayed average accuracies of up to 98.61% and 93.25% on cross-validation and blind set, respectively. After the prediction of EC subclass, the specific metabolic enzyme (EC) is identified using a molecular similarity search. The performance was further evaluated on an independent set of FDA-approved drugs and other clinically important molecules. To our knowledge, this is the only available approach implemented as 'DrugBug' tool for the prediction of xenobiotic/drug metabolism by metabolic enzymes of human gut microbiota.
人类肠道微生物群由一群具有巨大代谢潜力的微生物物种组成，这些微生物可以改变口服药物的代谢，导致个体/人群对药物反应的特异性差异。考虑到人类肠道细菌及其代谢酶的巨大异质池，通过实验研究来研究特定物种对异源/药物代谢的贡献是一项具有挑战性的任务。因此，我们开发了一种新的计算方法来预测可能进行异源/药物分子生物转化的代谢酶和肠道细菌物种。为 491 种可获得的人类肠道细菌中的代谢酶构建了一个底物数据库。从这些底物中提取结构特性（指纹），并用于开发随机森林模型，这些模型在交叉验证和盲集上的平均准确度分别高达 98.61%和 93.25%。在预测 EC 亚类之后，使用分子相似性搜索来识别特定的代谢酶（EC）。还在 FDA 批准的药物和其他临床重要分子的独立数据集上评估了性能。据我们所知，这是唯一可作为“DrugBug”工具使用的方法，用于预测人类肠道微生物群的代谢酶对异源/药物的代谢。
