BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification
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, distri...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-01-01
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| Series: | Journal of Cheminformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13321-018-0324-5 |
| _version_ | 1811325611547820032 |
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| author | Yannick Djoumbou-Feunang Jarlei Fiamoncini Alberto Gil-de-la-Fuente Russell Greiner Claudine Manach David S. Wishart |
| author_facet | Yannick Djoumbou-Feunang Jarlei Fiamoncini Alberto Gil-de-la-Fuente Russell Greiner Claudine Manach David S. Wishart |
| author_sort | Yannick Djoumbou-Feunang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T14:37:22Z |
| format | Article |
| id | doaj.art-c401afe322324cfa9dc449a0a11835ae |
| institution | Directory Open Access Journal |
| issn | 1758-2946 |
| language | English |
| last_indexed | 2024-04-13T14:37:22Z |
| publishDate | 2019-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj.art-c401afe322324cfa9dc449a0a11835ae2022-12-22T02:43:01ZengBMCJournal of Cheminformatics1758-29462019-01-0111112510.1186/s13321-018-0324-5BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identificationYannick Djoumbou-Feunang0Jarlei Fiamoncini1Alberto Gil-de-la-Fuente2Russell Greiner3Claudine Manach4David S. Wishart5Department of Biological Sciences, University of AlbertaINRA, Human Nutrition Unit, Université Clermont AuvergneDepartment of Information Technology, CEU San Pablo UniversityDepartment of Computing Science, University of AlbertaINRA, Human Nutrition Unit, Université Clermont AuvergneDepartment of Biological Sciences, University of AlbertaAbstract 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.http://link.springer.com/article/10.1186/s13321-018-0324-5Metabolism predictionMetabolite identificationBiotransformationMicrobial degradationMass spectrometryMachine learning |
| spellingShingle | Yannick Djoumbou-Feunang Jarlei Fiamoncini Alberto Gil-de-la-Fuente Russell Greiner Claudine Manach David S. Wishart BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification Journal of Cheminformatics Metabolism prediction Metabolite identification Biotransformation Microbial degradation Mass spectrometry Machine learning |
| title | BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification |
| title_full | BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification |
| title_fullStr | BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification |
| title_full_unstemmed | BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification |
| title_short | BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification |
| title_sort | biotransformer a comprehensive computational tool for small molecule metabolism prediction and metabolite identification |
| topic | Metabolism prediction Metabolite identification Biotransformation Microbial degradation Mass spectrometry Machine learning |
| url | http://link.springer.com/article/10.1186/s13321-018-0324-5 |
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