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BioTransformer 3.0-a web server for accurately predicting metabolic transformation products.
Wishart, David S; Tian, Siyang; Allen, Dana; Oler, Eponine; Peters, Harrison; Lui, Vicki W; Gautam, Vasuk; Djoumbou-Feunang, Yannick; Greiner, Russell; Metz, Thomas O.
Afiliación
  • Wishart DS; Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
  • Tian S; Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada.
  • Allen D; Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2B7, Canada.
  • Oler E; Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2H7, Canada.
  • Peters H; Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.
  • Lui VW; Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
  • Gautam V; Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
  • Djoumbou-Feunang Y; Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
  • Greiner R; Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
  • Metz TO; Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
Nucleic Acids Res ; 50(W1): W115-W123, 2022 07 05.
Article en En | MEDLINE | ID: mdl-35536252
BioTransformer 3.0 (https://biotransformer.ca) 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 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 less than a minute. 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 biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40-50% more accurate, far less prone to combinatorial 'explosions' and much more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Xenobióticos / Biología Computacional Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Nucleic Acids Res Año: 2022 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Xenobióticos / Biología Computacional Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Nucleic Acids Res Año: 2022 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Reino Unido