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

Journal of cheminformatics, 2019-01, Vol.11 (1), p.2-25, Article 2 [Peer Reviewed Journal]

The Author(s) 2019 ;COPYRIGHT 2019 BioMed Central Ltd. ;Journal of Cheminformatics is a copyright of Springer, (2019). All Rights Reserved. © 2019. 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. ;Attribution ;ISSN: 1758-2946 ;EISSN: 1758-2946 ;DOI: 10.1186/s13321-018-0324-5 ;PMID: 30612223

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Title:
BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification
Author: Djoumbou-Feunang, Yannick ; Fiamoncini, Jarlei ; Gil-de-la-Fuente, Alberto ; Greiner, Russell ; Manach, Claudine ; Wishart, David S.
Subjects: Analytical chemistry ; Bioinformatics ; Biotransformation ; Chemical Sciences ; Chemistry ; Chemistry and Materials Science ; Computation ; Computational Biology/Bioinformatics ; Computer applications ; Computer Applications in Chemistry ; Computer programs ; Computer Science ; Data processing ; Documentation and Information in Chemistry ; Environmental degradation ; Enzymes ; Epicatechin ; Excretion ; Human tissues ; Identification ; Knowledge-based systems ; Learning algorithms ; Liver ; Machine learning ; Mass spectrometry ; Mass spectroscopy ; Metabolism ; Metabolism prediction ; Metabolite identification ; Metabolites ; Metabolomics ; Microbial degradation ; Microbiota ; Microbiota (Symbiotic organisms) ; Open access ; Pesticides ; Physiological aspects ; Predictions ; Software ; Soil water ; Spectroscopy ; State of the art ; Supplementation ; Theoretical and Computational Chemistry ; Tissues ; Toxicity ; Transformation
Is Part Of: Journal of cheminformatics, 2019-01, Vol.11 (1), p.2-25, Article 2
Description: 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.
Publisher: Cham: Springer International Publishing
Language: English
Identifier: ISSN: 1758-2946
EISSN: 1758-2946
DOI: 10.1186/s13321-018-0324-5
PMID: 30612223
Source: SpringerOpen
Geneva Foundation Free Medical Journals at publisher websites
Hyper Article en Ligne (HAL) (Open Access)
PubMed Central
ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
DOAJ Directory of Open Access Journals

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

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