Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction

Li, Feiran; Yuan, Le; Lu, Hongzhong; Li, Gang; Yu, Chen; Engqvist, Martin K. M.; Kerkhoven, Eduard J.; Nielsen, Jens

doi:10.1038/s41929-022-00798-z

Cited by 196 publications

(244 citation statements)

References 70 publications

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“…Unfortunately, there is little data on both for C. glutamicum. There are three ways to improve the coverage of enzyme kinetic parameters in the model: 1) directly populate unknown reactions with mean or median values of enzyme kinetic parameters from other reactions [16][17][18]; 2) expand the EC number annotation information of model reactions using EC number prediction tools [54,55]; and 3) directly predict reactions with unknown parameters based on existing enzyme kinetic parameters via machine learning or deep learning approaches [25,56]. In addition, the kinetic data are mainly sourced from the BRENDA and SABIO-RK databases, which mostly collect in vitro measurements that differ somewhat from the in vivo data.…”

Section: Discussionmentioning

confidence: 99%

“…In the enzyme-constrained models, kcat and molecular weight (MW) of an enzyme set constraints on the fluxes of the reactions catalyzed by that enzyme. Previous studies have made efforts to automatically acquire kcat values from databases and fill missing values using methods like machine learning [25]. In contrast, few studies paid attention to molecular weight.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Construction and Analysis of an Enzyme‐constrained Metabolic Model of <em>Corynebacterium glutamicum</em>

Niu¹,

Mao²,

Mao³

et al. 2022

Preprint

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Genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only consid-ers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experi-mentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineer-ing targets. Here we present the first genome-scale enzyme-constrained model (eciCW773) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of pheno-types and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used eciCW773 to identify several gene modifica-tion targets for L-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Construction and Analysis of an Enzyme‐constrained Metabolic Model of <em>Corynebacterium glutamicum</em>

Niu¹,

Mao²,

Mao³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, the gradient descent algorithm changes the turnover numbers of all the enzymes simultaneously, thus the imputed enzyme concentrations effectively get changed relative to the initial estimates, attenuating dramatic changes in turnover number estimates of single reactions. Thus, given the increasing number of machine learning generated turnover number estimates [14,48], this algorithm is well suited as a refinement step to increase their accuracy.…”

Section: Estimating Enzyme Turnover Numbersmentioning

confidence: 99%

Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models

Wilken

Besançon

Kratochvíl

et al. 2022

Preprint

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Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis.

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“…In the enzyme-constrained models, k cat and molecular weight (MW) of an enzyme set constraints on the fluxes of the reactions catalyzed by that enzyme. Previous studies have made efforts to automatically acquire k cat values from databases and fill missing values using methods like machine learning [ 25 ]. In contrast, few studies have paid attention to molecular weight.…”

Section: Introductionmentioning

confidence: 99%

Construction and Analysis of an Enzyme-Constrained Metabolic Model of Corynebacterium glutamicum

Niu

Mao

et al. 2022

Biomolecules

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The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experimentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineering targets. Here, we present the first genome-scale enzyme-constrained model (ecCGL1) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using a ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of phenotypes and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used the ecCGL1 to identify several gene modification targets for l-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.

show abstract

Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction

Cited by 196 publications

References 70 publications

Construction and Analysis of an Enzyme‐constrained Metabolic Model of <em>Corynebacterium glutamicum</em>

Construction and Analysis of an Enzyme‐constrained Metabolic Model of <em>Corynebacterium glutamicum</em>

Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models

Construction and Analysis of an Enzyme-Constrained Metabolic Model of Corynebacterium glutamicum

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