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EnzymeML
EnzymeML is a free and open standard based XML markup interchange format for enzyme kinetics.
ID:78595UploaderBioTreasury
2023.09.05
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Abstract
The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org .
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Publication
EnzymeML: seamless data flow and modeling of enzymatic data
Simone Lauterbach,Hannah Dienhart,Jan Range,Stephan Malzacher,Jan-Dirk Spöring,Dörte Rother,Maria Filipa Pinto,Pedro Martins,Colton E. Lagerman,Andreas S. Bommarius,Amalie Vang Høst,John M. Woodley,Sandile Ngubane,Tukayi Kudanga,Frank T. Bergmann,Johann M. Rohwer,Dorothea Iglezakis,Andreas Weidemann,Ulrike Wittig,Carsten Kettner,Neil Swainston,Santiago Schnell,Jürgen PleissNature Methods2023
Cited by 33 articles
Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments
Maximilian Gantz, Stefanie Neun, Elliot J. Medcalf, Liisa D. van Vliet, Florian Hollfelder Chemical Reviews2023
PMID:37126602
PMCID:PMC10176489
Impact Factor:64.2
From nature to industry: Harnessing enzymes for biocatalysis
R Buller, S Lutz, R J Kazlauskas, R Snajdrova, J C Moore, U T Bornscheuer Science2023
PMID:37995253
Impact Factor:47.3
The STRENDA Biocatalysis Guidelines for cataloguing metadata
Stephan Malzacher, Dominik Meißner, Jan Range, Zvjezdana Findrik Blažević, Katrin Rosenthal, John M. Woodley, Roland Wohlgemuth, Peter Wied, Bernd Nidetzky, Robert T. Giessmann, Kridsadakorn Prakinee, Pimchai Chaiyen, Andreas S. Bommarius, Johann M. Rohwer, Rodrigo O. M. A. de Souza, Peter J. Halling, Jürgen Pleiss, Carsten Kettner, Dörte Rother Nature Catalysis2024
Impact Factor:48.3
Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering
Wen Jun Xie, Arieh Warshel National Science Review2023
PMID:38299119
PMCID:PMC10829072
Impact Factor:18.1
Machine learning-guided evolution of pyrrolysyl-tRNA synthetase for improved incorporation efficiency of diverse noncanonical amino acids
Qunfeng Zhang, Ling Jiang, Yadan Niu, Yujie Li, Wanyi Chen, Jingxi Cheng, Haote Ding, Binbin Chen, Ke Liu, Jiawen Cao, Junli Wang, Shilin Ye, Lirong Yang, Jianping Wu, Gang Xu, Jianping Lin, Haoran Yu Nature Communications2025
PMID:40681550
PMCID:PMC12274524
Impact Factor:18.1
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