EnzymeML—a data exchange format for biocatalysis and enzymology

Abstract

EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modelling tools, and databases. EnzymeML supports the scientific community by introducing a standardised data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An Application Programming Interface in Python and Java supports the integration of applications.The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modelling using the modelling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.

Enzyme Immobilization and Co-Immobilization: Main Framework, Advances and Some Applications

“…Strategies allowing for controlled reversibility of immobilization have also been described [44][45][46]. Lastly, different methodologies have been suggested towards a more rational approach for enzyme immobilization, which include: detailed knowledge of the physical and chemical properties of both enzyme and carrier (e.g., distribution of hydrophobic and hydrophilic regions in the carrier and hydrophobic and polar regions in the surface of individual enzyme; carrier porosity, including pore size distribution, structure and volume carrier chemical and mechanical endurance of operational conditions); generation of databases and use of bioinformatics and other computational tools for improved characterization of enzymes and corresponding catalytic features; factorial planning; and protein engineering to enable a specific enzyme orientation on a carrier [37,[47][48][49][50][51]. Overall, once optimized, enzyme immobilization is intended to provide a biocatalyst formulation that may lead to total turnover number (TTN), defined as the total moles of product produced per mole of enzyme over the entire lifetime of the enzyme, over either 10 3 (for expensive products produced at small scale) or 5 × 10 5 -5 × 10 6 (for low-cost products/commodities), according to the thresholds required in industry [52][53][54].…”

Putting precision and elegance in enzyme immobilisation with bio-orthogonal chemistry

“…Much research is devoted, therefore, to designing compartmentalised cascade processes with isolated enzymes in vitro that emulate this exquisite orchestration of multi-enzyme metabolic pathways in vivo . 253 The use of enzyme data bases such as EnzymeML and SABIO-RK 254 in combination with machine-assisted learning 255 and the use of microfluidics and robotics will play an important role in designing the next generation of biotechnological processes involving enzyme cascades in compartments. 256 We expect that the design of efficient bio-orthogonal methods for precision co-immobilisation of multiple enzymes will play an indispensible role in these developments.…”

Development of an Ontology for Biocatalysis

“…However, no database exists to date that allows for a quick search of biocatalytic activity data, hence, the literature has to be screened manually. First steps into this direction are done with EnzymeML and its graphical user interface BioCatHub towards standardized biocatalytic data and web-based storage to allow for sharing of scientific data via XML (Extensible Markup Language) documents [10,[13][14]. Accessibility and integration of data is important to not only assure reproducibility [15], but also to enable new findings with already available data sets.…”