EnzymeML: seamless data flow and modeling of enzymatic data

Lauterbach, Simone; Dienhart, H.; Range, Jan; Malzacher, Stephan; Spöring, Jan-Dirk; Rother, Dörte; Pinto, Maria Filipa; Martins, Pedro M.; Lagerman, Colton E.; Bommarius, Andreas S.; Høst, Amalie Vang; Woodley, John M.; Ngubane, Sandile; Kudanga, Tukayi; Bergmann, Frank; Rohwer, Johann M.; Iglezakis, Dorothea; Weidemann, Andreas; Wittig, Ulrike; Kettner, Carsten; Swainston, Neil; Schnell, Santiago; Pleiss, Jürgen

doi:10.1038/s41592-022-01763-1

Cited by 15 publications

(18 citation statements)

References 27 publications

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“…Engineering approach to kinetic model development involves parsimony of parameterization as a guiding principle (Almquist et al, 2014; Gernaey et al, 2010; Lauterbach et al, 2023; Lencastre Fernandes et al, 2013; Sadino‐Riquelme et al, 2020). Considering a bottom‐up strategy of stepwise increase in model complexity, the minimum feature of basal model is that of an enzymatic net rate (flux) under control of mass action.…”

Section: Resultsmentioning

confidence: 99%

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Pushing the boundaries of phosphorylase cascade reaction for cellobiose production I: Kinetic model development

Sigg,

Klimacek,

Nidetzky

2023

Biotech & Bioengineering

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One‐pot cascade reactions of coupled disaccharide phosphorylases enable an efficient transglycosylation via intermediary α‐d‐glucose 1‐phosphate (G1P). Such transformations have promising applications in the production of carbohydrate commodities, including the disaccharide cellobiose for food and feed use. Several studies have shown sucrose and cellobiose phosphorylase for cellobiose synthesis from sucrose, but the boundaries on transformation efficiency that result from kinetic and thermodynamic characteristics of the individual enzyme reactions are not known. Here, we assessed in a step‐by‐step systematic fashion the practical requirements of a kinetic model to describe cellobiose production at industrially relevant substrate concentrations of up to 600 mM sucrose and glucose each. Mechanistic initial‐rate models of the two‐substrate reactions of sucrose phosphorylase (sucrose + phosphate → G1P + fructose) and cellobiose phosphorylase (G1P + glucose → cellobiose + phosphate) were needed and additionally required expansion by terms of glucose inhibition, in particular a distinctive two‐site glucose substrate inhibition of the cellobiose phosphorylase (fromCellulumonas uda). Combined with mass action terms accounting for the approach to equilibrium, the kinetic model gave an excellent fit and a robust prediction of the full reaction time courses for a wide range of enzyme activities as well as substrate concentrations, including the variable substoichiometric concentration of phosphate. The model thus provides the essential engineering tool to disentangle the highly interrelated factors of conversion efficiency in the coupled enzyme reaction; and it establishes the necessary basis of window of operation calculations for targeted optimizations toward different process tasks.

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Section: Resultsmentioning

confidence: 99%

Section: Flux Models M1 and M2mentioning

confidence: 99%

Pushing the boundaries of phosphorylase cascade reaction for cellobiose production I: Kinetic model development

Sigg,

Klimacek,

Nidetzky

2023

Biotech & Bioengineering

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“…More clones can be characterized in meaningful detail to draw up sequence- or structure–activity relationships and uncover mechanisms. The obtained kinetic data traces will also be useful for future modeling efforts when added into databases like EnzymeML . In addition to recording steady-state (Michaelis–Menten) or pre-steady-state kinetics, probing the acceptance of alternative promiscuous substrates, the effects of inhibitors, and the temperature stability of newly identified enzymes will be instructive.…”

Section: Characterizationmentioning

confidence: 99%

“…The obtained kinetic data traces will also be useful for future modeling efforts when added into databases like EnzymeML. 298 In addition to recording steady-state (Michaelis−Menten) or pre-steady-state kinetics, probing the acceptance of alternative promiscuous 195 substrates, the effects of inhibitors, and the temperature stability of newly identified enzymes will be instructive. Sequence−function studies will greatly benefit from such quantitative insights, and their future combination with structure prediction from deep learning approaches 299,300 should provide renewed impetus for protein engineering, perhaps even allowing for the reliable prediction of function.…”

Section: Characterizationmentioning

confidence: 99%

Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments

et al. 2023

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Novel and improved biocatalysts are increasingly sourced from libraries via experimental screening. The success of such campaigns is crucially dependent on the number of candidates tested. Water-in-oil emulsion droplets can replace the classical test tube, to provide in vitro compartments as an alternative screening format, containing genotype and phenotype and enabling a readout of function. The scale-down to micrometer droplet diameters and picoliter volumes brings about a >10 7 -fold volume reduction compared to 96-well-plate screening. Droplets made in automated microfluidic devices can be integrated into modular workflows to set up multistep screening protocols involving various detection modes to sort >10 7 variants a day with kHz frequencies. The repertoire of assays available for droplet screening covers all seven enzyme commission (EC) number classes, setting the stage for widespread use of droplet microfluidics in everyday biochemical experiments. We review the practicalities of adapting droplet screening for enzyme discovery and for detailed kinetic characterization. These new ways of working will not just accelerate discovery experiments currently limited by screening capacity but profoundly change the paradigms we can probe. By interfacing the results of ultrahigh-throughput droplet screening with next-generation sequencing and deep learning, strategies for directed evolution can be implemented, examined, and evaluated. CONTENTSAA 11.6.2. Combining High-Throughput Selections with High-Throughput Analysis AB 12. Conclusions

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“…It is therefore very difficult to assess their individual veracity, and to remove those that appear to be exceptions, or even invalid, from the training sets. In order to provide access to more homogeneous, and above all, more reproducible and verifiable data sets, several initiatives are ongoing, notably around standardizing the measurement of enzyme kinetic parameters using common conditions, [10] and building more complete databases, requiring a minimal set of parameters for the addition of new entries, such as STRENDA DB. [11] In fact, the main difference between this new database and the more general databases mentioned above is that the latter is oriented towards the use of enzymes in synthesis, rather than towards general enzymology or even the global study of proteins.…”

Section: Introductionmentioning

confidence: 99%

Data‐Driven Protein Engineering for Improving Catalytic Activity and Selectivity

Ao,

Dörr,

Menke

et al. 2023

ChemBioChem

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Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme‐substrate‐catalysis performance relationships aiming to improve enzymes through data‐driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.

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EnzymeML: seamless data flow and modeling of enzymatic data

Cited by 15 publications

References 27 publications

Pushing the boundaries of phosphorylase cascade reaction for cellobiose production I: Kinetic model development

Pushing the boundaries of phosphorylase cascade reaction for cellobiose production I: Kinetic model development

Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments

Data‐Driven Protein Engineering for Improving Catalytic Activity and Selectivity

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