Abstract

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.

Details

Title
A novel approach for the prediction of species-specific biotransformation of xenobiotic/drug molecules by the human gut microbiota
Author
Sharma, Ashok K 1 ; Jaiswal, Shubham K 1 ; Chaudhary, Nikhil 1 ; Sharma, Vineet K 1 

 Metagenomics and Systems Biology Laboratory, Indian Institute of Science Education and Research, Bhopal, Madhya Pradesh, India 
Pages
1-13
Publication year
2017
Publication date
Aug 2017
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1957859023
Copyright
© 2017. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.