MITE: the Minimum Information about a Tailoring Enzyme database for capturing specialized metabolite biosynthesis

Secondary or specialized metabolites show extraordinary structural diversity and potent biological activities relevant for clinical and industrial applications. The biosynthesis of these metabolites usually starts with the assembly of a core “scaffold”, which is subsequently modified by tailoring enzymes to define the molecule’s final structure and, in turn, its biological activity profile. Knowledge about reaction and substrate specificity of tailoring enzymes is essential for understanding and computationally predicting metabolite biosynthesis, but this information is usually scattered in the literature. Here, we present MITE, the Minimum Information about a Tailoring Enzyme database. MITE employs a comprehensive set of parameters to annotate tailoring enzymes, defining substrate and reaction specificity by the expressive reaction SMARTS chemical pattern language. Both human- and machine-readable, MITE can be used as a knowledgebase, for in silico biosynthesis, or to train machine learning applications, and tightly integrates with existing resources. Designed as a community-driven and open resource, MITE employs a rolling release model of data curation and expert review. MITE is freely accessible at https://mite.bioinformatics.nl/.

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Publication Details

Journal
ChemRxiv
Published
2025-08-20
DOI
https://doi.org/10.26434/chemrxiv-2024-78mtl-v2
Primary Topic
Microbial Metabolic Engineering and Bioproduction
Type
article
Field-Weighted Citation Impact
0.00

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article

MITE: the Minimum Information about a Tailoring Enzyme database for capturing specialized metabolite biosynthesis

Anna M. Köhler, Aleksandra Korenskaia, Hannah E. Augustijn, Jennifer Gerke et al.
ChemRxiv
Microbial Metabolic Engineering and Bioproduction
article

MITE: the Minimum Information about a Tailoring Enzyme database for capturing specialized metabolite biosynthesis

Anna M. Köhler, Aleksandra Korenskaia, Hannah E. Augustijn, Jennifer Gerke, Kai Blin, Ji-Yeon Hwang, Barbara R. Terlouw, Max Crüsemann, Christine Beemelmanns, Adriano Rutz, Fabrizio Alberti, Marnix H. Medema, Daniel Yuri Akiyama, Friederike Biermann, Marc Feuermann, Noel Kubach, Surinder S. Narula, Catarina Loureiro, Hellen Bertoletti Barbieri, Nadine Ziemert, Russell J. Cox, Jingwei Yu, Byung Tae Lee, Paul M. D’Agostino, David Meijer, Nika Sokolova, Simon Sieber, Justin J. J. van der Hooft, Riccardo Iacovelli, Jorge C. Navarro-Muñoz, Nicole E. Avalon, Mitja M. Zdouc, Giang‐Son Nguyen, Sven T. Sowa, Tilmann Weber, César Aguilar, Martin Klapper, Alan Bridge, Júlio César Jeronimo Barbosa, Judith Szenei, Jonathan E Holme, Heiner G Weddeling, Sunaina Paliyal, Navneet Kaur, Shrikant Mantri, L.G. Rao, Karina Garcia, Mohit Panghal, Daniel Probst, Esteban Charria Girón
article en

Abstract

Secondary or specialized metabolites show extraordinary structural diversity and potent biological activities relevant for clinical and industrial applications. The biosynthesis of these metabolites usually starts with the assembly of a core “scaffold”, which is subsequently modified by tailoring enzymes to define the molecule’s final structure and, in turn, its biological activity profile. Knowledge about reaction and substrate specificity of tailoring enzymes is essential for understanding and computationally predicting metabolite biosynthesis, but this information is usually scattered in the literature. Here, we present MITE, the Minimum Information about a Tailoring Enzyme database. MITE employs a comprehensive set of parameters to annotate tailoring enzymes, defining substrate and reaction specificity by the expressive reaction SMARTS chemical pattern language. Both human- and machine-readable, MITE can be used as a knowledgebase, for in silico biosynthesis, or to train machine learning applications, and tightly integrates with existing resources. Designed as a community-driven and open resource, MITE employs a rolling release model of data curation and expert review. MITE is freely accessible at https://mite.bioinformatics.nl/.

ChemRxiv
Goethe University Frankfurt (DE), Leibniz University Hannover (DE), Leiden University (NL), SIB Swiss Institute of Bioinformatics (CH), Target (United States) (US), University of Bonn (DE), University of Arizona (US), Scripps Institution of Oceanography (US), Korea Advanced Institute of Science and Technology (KR), University of Groningen (NL), SINTEF (NO), Universidade Estadual de Campinas (UNICAMP) (BR), University of Basel (CH), Carnegie Institution for Science (US), ZHAW Zurich University of Applied Sciences (CH), University of Zurich (CH), University of California, Irvine (US), University of Johannesburg (ZA), Novo Nordisk Foundation (DK), Southern University of Science and Technology (CN), ETH Zurich (CH), University of Warwick (GB), Regional Centre for Biotechnology (IN), LOEWE Centre for Translational Biodiversity Genomics (DE), Helmholtz Centre for Infection Research (DE), Irvine University (US), National Cancer Institute (US), Helmholtz Institute for Pharmaceutical Research Saarland (DE), Center for Cancer Research (US), Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie e. V. - Hans-Knöll-Institut (HKI) (DE), BRIC-National Agri-Food and Biomanufacturing Institute (IN), University of Tübingen (DE), VTT Technical Research Centre of Finland (FI), Wageningen University & Research (NL), Saarland University (DE), Technical University of Denmark (DK), Tecnológico de Monterrey (MX)
Deutsches Zentrum für Infektionsforschung, UK Research and Innovation, Australian Government, European Commission, National Research Foundation, Department of Biotechnology, Ministry of Science and Technology, India, University Grants Commission, Deutsche Forschungsgemeinschaft, Danmarks Grundforskningsfond, Fundação de Amparo à Pesquisa do Estado de São Paulo, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Novo Nordisk, Ministry of Education, India, Novo Nordisk Fonden, National Institutes of Health, HORIZON EUROPE Framework Programme, Staatssekretariat für Bildung, Forschung und Innovation
Openalex Percentile: Top 81%
Microbial Metabolic Engineering and Bioproduction
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