Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale

Hu, Mengqi; Dinh, Hoang V.; Shen, Yihui; Suthers, Patrick F.; Foster, Charles; Call, Catherine M.; Ye, Xuanjia; Pratas, Jimmy; Fatma, Zia; Zhao, Huimin; Rabinowitz, Joshua D.; Maranas, Costas D.

doi:10.1016/j.ymben.2023.01.001

Cited by 14 publications

(9 citation statements)

References 95 publications

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“…In contrast to this procedure, PRESTO corrects at once the turnover numbers of multiple enzymes that are measured in all investigated conditions by simultaneously leveraging the data from the different conditions, considerably reducing runtime and the number of solved problems. As a result, rather than deriving condition-specific corrected k cat values, which are difficult to use in making predictions for unseen scenarios or for building large-scale kinetic metabolic models 24,25 , PRESTO results in a single set of corrected k cat values.…”

Section: Geckomentioning

confidence: 99%

Data integration across conditions improves turnover number estimates and metabolic predictions

et al. 2023

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Turnover numbers characterize a key property of enzymes, and their usage in constraint-based metabolic modeling is expected to increase the prediction accuracy of diverse cellular phenotypes. In vivo turnover numbers can be obtained by integrating reaction rate and enzyme abundance measurements from individual experiments. Yet, their contribution to improving predictions of condition-specific cellular phenotypes remains elusive. Here, we show that available in vitro and in vivo turnover numbers lead to poor prediction of condition-specific growth rates with protein-constrained models of Escherichia coli and Saccharomyces cerevisiae, particularly when protein abundances are considered. We demonstrate that correction of turnover numbers by simultaneous consideration of proteomics and physiological data leads to improved predictions of condition-specific growth rates. Moreover, the obtained estimates are more precise than corresponding in vitro turnover numbers. Therefore, our approach provides the means to correct turnover numbers and paves the way towards cataloguing kcatomes of other organisms.

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Section: Geckomentioning

confidence: 99%

Data integration across conditions improves turnover number estimates and metabolic predictions

et al. 2023

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“…A complementary approach is kinetic modelling based on Ordinary Differential Equations (ODEs). Several works have expanded such models to the genome scale [21][22][23][24][25] , with some success in predicting the dynamics of some model microbes. However, the applicability of genome-scale kinetic models remains limited, as they require substantial strain and condition-specific fine tuning 26 .…”

Section: Introductionmentioning

confidence: 99%

Modelling dynamic host-pathway interactions at the genome scale

Merzbacher,

Aodha,

Oyarzún

2024

Preprint

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Pathway engineering offers a promising avenue for sustainable chemical production. The design of efficient production systems requires understanding complex host-pathway interactions that shape the metabolic phenotype. While genome-scale metabolic models are widespread tools for studying static host-pathway interactions, it remains a challenge to predict dynamic effects such as metabolite accumulation or enzyme overexpression during the course of fermentation. Here, we propose a novel strategy to integrate kinetic pathway models with genome-scale metabolic models of the production host. Our method enables the simulation of the local nonlinear dynamics of pathway enzymes and metabolites, informed by the global metabolic state of the host as predicted by Flux Balance Analysis (FBA). To reduce computational costs, we make extensive use of surrogate machine learning models to replace FBA calculations, achieving simulation speed-ups of at least two orders of magnitude. Through case studies on two production pathways inEscherichia coli, we demonstrate the consistency of our simulations and the ability to predict metabolite dynamics under genetic perturbations and various carbon sources. We showcase the utility of our method for screening dynamic control circuits through large-scale parameter sampling and mixed-integer optimization. Our work links together genome-scale and kinetic models into a comprehensive framework for computational strain design.

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“…Finally, kinetic models, by relating enzyme kinetics to the concentration of metabolites and enzyme levels within a cell, can be used to both describe and redesign metabolism 18 . Advances in automated functional annotation of proteins have enabled building metabolic models with a genome-wide coverage of cellular metabolism 19,20 . However, efficient kinetic parameterization to match observed fluxomic, proteomic and/or metabolomic datasets remains a bottleneck 21 .…”

Section: Introductionmentioning

confidence: 99%

“…Advances in automated functional annotation of proteins have enabled building metabolic models with a genome-wide coverage of cellular metabolism 19,20 . However, efficient kinetic parameterization to match observed fluxomic, proteomic and/or metabolomic datasets remains a bottleneck 21 .…”

Section: Introductionmentioning

confidence: 99%

CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parametersk_cat,K_mandK_i

Boorla,

Maranas

2024

Preprint

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Quantification of enzymatic activities still heavily relies on experimental assays, which can be expensive and time-consuming. Therefore, methods that enable accurate predictions of enzyme activity can serve as effective digital twins. A few recent studies have shown the possibility of training machine learning (ML) models for predicting the enzyme turnover numbers (kcat) and Michaelis constants (Km) using only features derived from enzyme sequences and substrate chemical topologies by training onin vitromeasurements. However, several challenges remain such as lack of standardized training datasets, evaluation of predictive performance on out-of-distribution examples, and model uncertainty quantification. Here, we introduce CatPred, a comprehensive framework for ML prediction ofin vitroenzyme kinetics. We explored different learning architectures and feature representations for enzymes including those utilizing pretrained protein language model features and three-dimensional structural features. We systematically evaluate the performance of trained models for predictingkcat,Km, and inhibition constants (Ki) of enzymatic reactions on held-out test sets with a special emphasis on out-of-distribution test samples (corresponding to enzyme sequences dissimilar from those encountered during training). CatPred assumes a probabilistic regression approach offering probability distributions instead of single value predictions. Results on unseen data confirm that accuracy in enzyme parameter predictions made by CatPred positively correlate with lower predicted variances. Incorporating pre-trained language model features are found to be enabling for achieving robust performance on out-of-distribution samples. Test evaluations on both held-out and out-of-distribution test datasets confirm that CatPred performs at least competitively with existing methods while simultaneously offering robust uncertainty quantification. CatPred offers wider scope and larger data coverage (∼23k, 41k, 12k data-points) respectively forkcat, Kmand Ki. A web-resource to use the trained models is available at:https://tiny.cc/catpred

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Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale

Cited by 14 publications

References 95 publications

Data integration across conditions improves turnover number estimates and metabolic predictions

Data integration across conditions improves turnover number estimates and metabolic predictions

Modelling dynamic host-pathway interactions at the genome scale

CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parametersk_cat,K_mandK_i

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Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale

Cited by 14 publications

References 95 publications

Data integration across conditions improves turnover number estimates and metabolic predictions

Data integration across conditions improves turnover number estimates and metabolic predictions

Modelling dynamic host-pathway interactions at the genome scale

CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameterskcat,KmandKi

Contact Info

Product

Resources

About

CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parametersk_cat,K_mandK_i