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J Cheminform. 2019 Jan 5;11(1):2. doi: 10.1186/s13321-018-0324-5.

BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification.

Author information

1
Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G 2E9, Canada.
2
INRA, Human Nutrition Unit, Université Clermont Auvergne, 63000, Clermont-Ferrand, France.
3
Department of Food and Experimental Nutrition, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil.
4
Department of Information Technology, CEU San Pablo University, Madrid, Spain.
5
Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada.
6
Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, T6G 2E8, Canada.
7
Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G 2E9, Canada. david.wishart@ualberta.ca.
8
Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada. david.wishart@ualberta.ca.

Abstract

BACKGROUND:

A number of computational tools for metabolism prediction have been developed over the last 20 years to predict the structures of small molecules undergoing biological transformation or environmental degradation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, although there is now a growing interest in using such tools to facilitate metabolomics and exposomics studies. However, their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance.

RESULTS:

To address these limitations, we have developed BioTransformer, a freely available software package for accurate, rapid, and comprehensive in silico metabolism prediction and compound identification. BioTransformer combines a machine learning approach with a knowledge-based approach to predict small molecule metabolism in human tissues (e.g. liver tissue), the human gut as well as the environment (soil and water microbiota), via its metabolism prediction tool. A comprehensive evaluation of BioTransformer showed that it was able to outperform two state-of-the-art commercially available tools (Meteor Nexus and ADMET Predictor), with precision and recall values up to 7 times better than those obtained for Meteor Nexus or ADMET Predictor on the same sets of pharmaceuticals, pesticides, phytochemicals or endobiotics under similar or identical constraints. Furthermore BioTransformer was able to reproduce 100% of the transformations and metabolites predicted by the EAWAG pathway prediction system. Using mass spectrometry data obtained from a rat experimental study with epicatechin supplementation, BioTransformer was also able to correctly identify 39 previously reported epicatechin metabolites via its metabolism identification tool, and suggest 28 potential metabolites, 17 of which matched nine monoisotopic masses for which no evidence of a previous report could be found.

CONCLUSION:

BioTransformer can be used as an open access command-line tool, or a software library. It is freely available at https://bitbucket.org/djoumbou/biotransformerjar/ . Moreover, it is also freely available as an open access RESTful application at www.biotransformer.ca , which allows users to manually or programmatically submit queries, and retrieve metabolism predictions or compound identification data.

KEYWORDS:

Biotransformation; Enzyme-substrate specificity; Knowledge-based system; Machine learning; Mass spectrometry; Metabolic pathway; Metabolism prediction; Metabolite identification; Microbial degradation; Structure-based classification

PMID:
30612223
DOI:
10.1186/s13321-018-0324-5
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