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, 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. Electronic supplementary material The online version of this article (10.1186/s13321-018-0324-5) contains supplementary material, which is available to authorized users.

Compound Characterization and Metabolic Profile Elucidation after In Vitro Gastrointestinal and Hepatic Biotransformation of an Herniaria hirsuta Extract Using Unbiased Dynamic Metabolomic Data Analysis

“…Biotransformer was used as an additional software tool to predict phase II metabolites. The tool uses both a knowledge-based approach and a machine-learning-based approach to predict biotransformation [42]. The SMILES string of medicagenic acid was uploaded, and "Phase I Transformation" and "Phase II Transformation" were selected separately.…”

“…This suggests a bidesmosidic saponin with R 1 comprising uronic acid and deoxyhexose and R 2 comprising two pentosyl, two deoxyhexosyl and two hexosyl moieties, with a hexosyl moiety in a terminal position. Combining all the information with the retention time and literature data, the saponin with m/z 1703.71734 supports a molecular formula of C 76 H 120 O 42 and was tentatively identified as medicagenic acid attached to three deoxyhexosyl, two hexosyl, two pentosyl and a uronic acid moiety [15][16][17][18][19][20][21]. However, MS n does not provide enough information for absolute structural characterization.…”

Current status of retention time prediction in metabolite identification

“…PubChem contains numerous structures not of biological interest but can serve as a proxy of a very large molecular structure database with more than 100 million entries. Databases of comparable size can be generated using, say, in silico metabolism prediction, such as Metabolite In Silico Network Extensions (MINEs) [20] and BioTransformerDB [21].…”

Exploring Plant Sesquiterpene Diversity by Generating Chemical Networks

“…There are some related works as AFIR [23]/GRRM, RetroRules [22], and Biotransformer [21]. Isegawa et al [45] had made computational simulations predicting pathways for terpene formation from a humulyl cation and other intermediate molecules using AFIR [23]/GRRM of which the chemical results align with ours, which allowed for a cross-confirmation.…”

“…Table 1 shows an overview of some features of the 2Path-Sesquiterpenes and its related works. [45] Internal AFIR/GRRM Sesquiterpenes Internal -RetroRules [22] SMART SMIRKS General -RetroRules BioTransformer [21] SMART SMIRKS General -MetXBioDB…”