BioTransformer 3.0 – A Web Server for Accurately Predicting Metabolic Transformation Products

Journal Article · Tue May 10 00:00:00 EDT 2022 · Nucleic Acids Research (Online)

DOI:https://doi.org/10.1093/nar/gkac313· OSTI ID:1886912

Wishart, David S. ^[1]; Tian, Siyang ^[1]; Allen, Dana ^[2]; Oler, Eponine ^[1]; Peters, Harrison M. ^[1]; Lui, Vicki W. ^[1]; Gautam, Vasuk ^[1]; Djoumbou-Feunang, Yannick ^[3]; Greiner, Russell ^[1]; Metz, Thomas O. ^[4]

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1. University of Alberta, Edmonton, AB (Canada)
2. University of Alberta, Edmonton, AB (Canada); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
3. Corteva Agriscience, Indianapolis, IN (United States)
4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

BioTransformer 3.0 is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively viewable/sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes a few seconds. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0, uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human and/or environmental biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40-50% more accurate, much less prone to combinatorial “explosions” and far more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: Alberta Machine Intelligence Institute (AMII); Canadian Institutes of Health Research (CIHR); Genome Canada; National Institute of Environmental Health Sciences; National Institutes of Health (NIH); Natural Sciences and Engineering Research Council of Canada (NSERC); USDOE
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 1886912
• Report Number(s): PNNL-SA-177349
• Journal Information: Nucleic Acids Research (Online), Journal Name: Nucleic Acids Research (Online) Journal Issue: W1 Vol. 50; ISSN 1362-4962
• Publisher: Oxford University Press
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES