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

Previously published as preprints in chemRxiv version 1 and chemRxiv version 2.

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

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 (Simplified Molecular Input Line Entry System Arbitrary Target Specification) chemical pattern language. Both human and machine readable, MITE can be used as a knowledge base, 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/.