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Biotransformer: a Comprehensive Computational Tool for Small Molecule Metabolism Prediction and Metabolite Identification Y BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification Y. Djoumbou-Feunang, Jarlei Fiamoncini, A. Gil-De-La-Fuente, R. Greiner, Claudine Manach, D. S. Wishart To cite this version: Y. Djoumbou-Feunang, Jarlei Fiamoncini, A. Gil-De-La-Fuente, R. Greiner, Claudine Manach, et al.. BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification. Journal of Cheminformatics, Chemistry Central Ltd. and BioMed Central, 2019, 11, 10.1186/s13321-018-0324-5. hal-01997281 HAL Id: hal-01997281 https://hal.archives-ouvertes.fr/hal-01997281 Submitted on 28 Jan 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Djoumbou‑Feunang et al. J Cheminform (2019) 11:2 https://doi.org/10.1186/s13321-018-0324-5 Journal of Cheminformatics SOFTWARE Open Access BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identifcation Yannick Djoumbou‑Feunang1, Jarlei Fiamoncini2,3, Alberto Gil‑de‑la‑Fuente4, Russell Greiner5,6, Claudine Manach2 and David S. Wishart1,5* 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 deg‑ radation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxic‑ ity (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 identifcation. 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 metabo‑ lism 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, pesti‑ cides, 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 identifcation 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​://bitbu​cket.org/djoum​bou/biotr​ansfo​rmerj​ar/. Moreover, it is also freely available as an open access RESTful application at www.biotr​ansfo​rmer.ca, which allows users to manually or programmatically submit queries, and retrieve metabolism predictions or compound identifcation data. *Correspondence: [email protected] 1 Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada Full list of author information is available at the end of the article © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat​iveco​mmons​.org/licen​ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat​iveco​mmons​.org/ publi​cdoma​in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Djoumbou‑Feunang et al. J Cheminform (2019) 11:2 Page 2 of 25 Keywords: Metabolism prediction, Metabolite identifcation, Biotransformation, Microbial degradation, Mass spectrometry, Machine learning, Knowledge‑based system, Structure‑based classifcation, Metabolic pathway, Enzyme‑substrate specifcity Introduction elimination of metabolic by-products or xenobiotics. Metabolism is key to the production of energy (catabo- Xenobiotics are compounds such as pharmaceuticals lism), the generation of cellular building blocks (anab- and personal care products (PPCPs), pesticides, plant or olism) as well as the activation, detoxifcation, and food compounds, food additives, surfactants, solvents, elimination of metabolic by-products or xenobiotics. and other man-made or biologically foreign substances. Over the past 100 years, considerable efort has gone Tey constitute the largest portion of the human chemi- into determining the precise molecular details of primary cal exposome of which more than 95% remain unknown metabolism—i.e. the metabolic processes associated with or largely uncharacterized [2, 3]. In many cases, non- the production and breakdown of essential metabolites essential metabolites are the products of promiscuous or (e.g. lipids, amino acids, and steroids) [1]. Unfortunately, non-specifc enzymatic reactions [4, 5], microbial or gut somewhat less efort has been devoted to the characteri- metabolism [6, 7], liver-based phase I metabolism (oxida- zation or understanding of non-essential or secondary tion, reduction or hydrolysis) or general phase II metabo- metabolism and non-essential metabolites, partly due to lism (conjugation). Metabolism is known to signifcantly their much higher number, and greater structural com- infuence the pharmacokinetics and pharmacodynam- plexity, compared to primary metabolites. ics of xenobiotics and their derivatives within a biologi- Non-essential metabolites include metabolites gen- cal system [8] (Fig. 1). Moreover, given the diversity of erated through the activation, detoxifcation and biological systems that constitute our environment, it Fig. 1 Efects of metabolism on the pharmacokinetics and pharmacodynamics of small molecules. This fgure illustrates how metabolism of a xenobiotic can alter its pharmacodynamics (PD), including pharmacological activity (Act), and toxicological efects (Tox). Moreover, the nature of the resulting metabolites can infuence their involvement in pharmacokinetic processes (i.e. ADME absorption, distribution, metabolism, excretion). DETP diethylthiophosphate Djoumbou‑Feunang et al. J Cheminform (2019) 11:2 Page 3 of 25 is clear that understanding xenobiotic metabolism is of Chlorpyrifos can also lead to the generation of the critical to accurately linking chemistry and biology, and inactive metabolites 3,5,6-trichloro-2-pyridinol, and die- understanding the interactions between those biological thyl phosphorothioate (see Additional fle 1), via O-dear- systems and the environment. ylation [10]. Figure 2 partially describes the “life cycle of a xenobi- Once released from the human body into the environ- otic”, using pesticides as an example. Pesticides can be ment, the pool of xenobiotics and their derivatives often used to protect plants against insect pests, waterborne contaminate soil and water, where they are often further ailments, other plant competitors and parasites, thus degraded by soil and/or aquatic microbes. Te resulting enabling the production of larger amounts of high qual- metabolites, which are mostly unknown, can afect soil/ ity food products, while using less land [9]. In this regard, water microbial diversity, and soil fertility [12] and even pesticides contribute to a healthier way of life. However, re-enter the food chain [13, 14] (Fig. 2). Such a metabolic exposure to pesticides through inhalation (e.g. by farm- “life cycle” is applicable to other chemicals, such as phar- workers), skin contact, or ingestion of contaminated maceuticals, food additives, and other man-made prod- harvested products is known to cause harmful efects ucts, as highlighted by a steadily increasing number of (Fig. 2). For instance, the organophosphate pesticide independent studies [15, 16]. For these reasons, the char- Chlorpyrifos (see Additional fle 1) can be activated in acterization of xenobiotic metabolites, which has long humans to become the carcinogenic substance Chlorpy- been vitally important to the pharmaceutical industry rifos-oxon, through CYP450-catalyzed desulfurization [5], has become increasingly more important to the pesti- [10]. Moreover, exposure to Chlorpyrifos
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