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

Nature Catalysis, Journal Year: 2022, Volume and Issue: 5(8), P. 662 - 672

Published: June 16, 2022

Abstract Enzyme turnover numbers ( k cat ) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured data sparse noisy. Here we provide a deep learning approach (DLKcat) for high-throughput prediction metabolic enzymes from any organism merely substrate structures protein sequences. DLKcat can capture changes mutated identify amino acid residues with strong impact on values. We applied this predict genome-scale values more than 300 yeast species. Additionally, designed Bayesian pipeline parameterize enzyme-constrained models predicted The resulting outperformed the corresponding original previous pipelines in predicting phenotypes proteomes, enabled us explain phenotypic differences. model construction valuable tools uncover global trends of enzyme kinetics further elucidate metabolism large scale.

Language: Английский

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Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Biotechnology Advances, Journal Year: 2024, Volume and Issue: 74, P. 108401 - 108401

Published: June 27, 2024

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on distribution cellular resources. The rewiring microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed adverse physiological effects, such as impaired cell growth low product yields. Alleviating imposed undesirable changes has become an increasingly attractive approach constructing robust factories. In this review, we provide brief overview engineering, focusing specifically recent developments strategies diminishing while improving robustness yield. A variety examples are presented showcase promise engineering in facilitating design construction Finally, challenges limitations encountered discussed.

Language: Английский

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Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Metabolites, Journal Year: 2021, Volume and Issue: 12(1), P. 14 - 14

Published: Dec. 24, 2021

Genome-scale metabolic models (GEMs) enable the mathematical simulation of metabolism archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype phenotype by contextualizing different types Big Data (e.g., genomics, metabolomics, transcriptomics). In this review, we analyze available useful for modeling compile GEM reconstruction tools that integrate Data. We also discuss recent applications in industry research include predicting phenotypes, elucidating pathways, producing industry-relevant chemicals, identifying drug targets, generating knowledge to better understand host-associated diseases. addition up-to-date review currently available, assessed plethora developing new macromolecular expression dynamic resolution. Finally, provide perspective emerging areas, such as annotation, data managing, machine learning, which will play key role further utilization

Language: Английский

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Genome-scale modeling of yeast metabolism: retrospectives and perspectives

FEMS Yeast Research, Journal Year: 2022, Volume and Issue: 22(1)

Published: Jan. 1, 2022

Abstract Yeasts have been widely used for production of bread, beer and wine, as well bioethanol, but they also designed cell factories to produce various chemicals, advanced biofuels recombinant proteins. To systematically understand rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) reconstructed the model Saccharomyces cerevisiae nonconventional yeasts. Here, we review historical development GEMs together with their recent applications, including flux prediction, factory design, culture condition optimization multi-yeast comparative analysis. Furthermore, present an emerging effort, namely integration proteome constraints into GEMs, resulting in improved performance. At last, discuss challenges perspectives on constraints.

Language: Английский

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Changes in the chemical compositions and biological properties of kombucha beverages made from black teas and pineapple peels and cores

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: May 15, 2023

Abstract Several raw materials have been used as partial supplements or entire replacements for the main ingredients of kombucha to improve biological properties resulting beverage. This study pineapple peels and cores (PPC), byproducts processing, alternative instead sugar production. Kombuchas were produced from fusions black tea PPC at different ratios, their chemical profiles properties, including antioxidant antimicrobial activities, determined compared with control without supplementation. The results showed that contained high amounts beneficial substances, sugars, polyphenols, organic acids, vitamins, minerals. An analysis microbial community in a SCOBY (Symbiotic Cultures Bacteria Yeasts) using next-generation sequencing revealed Acetobacter Komagataeibacter most predominant acetic acid bacteria. Furthermore, Dekkera Bacillus also prominent yeast bacteria SCOBY. A comparative was performed products fermented fusion PPC, made infusion exhibited higher total phenolic content activity than kombucha. greater those control. volatile compounds contributed flavor, aroma, health such esters, carboxylic phenols, alcohols, aldehydes, ketones, detected PPC. shows exhibits potential supplement material functional

Language: Английский

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Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

Biotechnology Advances, Journal Year: 2024, Volume and Issue: 72, P. 108319 - 108319

Published: Jan. 26, 2024

Language: Английский

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Metal ion availability and homeostasis as drivers of metabolic evolution and enzyme function

Current Opinion in Genetics & Development, Journal Year: 2022, Volume and Issue: 77, P. 101987 - 101987

Published: Sept. 29, 2022

Language: Английский

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Trade‐offs between the instantaneous growth rate and long‐term fitness: Consequences for microbial physiology and predictive computational models

BioEssays, Journal Year: 2023, Volume and Issue: 45(10)

Published: Aug. 9, 2023

Abstract Microbial systems biology has made enormous advances in relating microbial physiology to the underlying biochemistry and molecular biology. By meticulously studying model microorganisms, particular Escherichia coli Saccharomyces cerevisiae , increasingly comprehensive computational models predict metabolic fluxes, protein expression, growth. The modeling rationale is that cells are constrained by a limited pool of resources they allocate optimally maximize fitness. As consequence, expression proteins at expense others, causing trade‐offs between cellular objectives such as instantaneous growth, stress tolerance, capacity adapt new environments. While current remarkably predictive for E. S. when grown laboratory environments, this may not hold other growth conditions microorganisms. In contribution, we therefore discuss relationship rate, resources, long‐term We uses limitations models, rapidly changing adverse propose classify strategies based on Grimes's CSR framework.

Language: Английский

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Cell factory design with advanced metabolic modelling empowered by artificial intelligence

Metabolic Engineering, Journal Year: 2024, Volume and Issue: 85, P. 61 - 72

Published: July 20, 2024

Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible determining whether a bio-based product succeeds or fails fierce competition with petroleum-based products. To date, one greatest challenges is creation high-performance factories consistent efficient manner. As so-called white-box models, numerous metabolic network models been developed used computational strain design. Moreover, great progress has made AI-powered engineering recent years. Both approaches advantages disadvantages. Therefore, deep integration AI crucial construction superior higher titres, yields production rates. The detailed applications latest advanced design summarized this review. Additionally, discussed. It anticipated that mechanistic powered by will pave way powerful chassis strains coming

Language: Английский

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Prediction and integration of metabolite-protein interactions with genome-scale metabolic models

Metabolic Engineering, Journal Year: 2024, Volume and Issue: 82, P. 216 - 224

Published: Feb. 15, 2024

Metabolites, as small molecules, can act not only substrates to enzymes, but also effectors of activity proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started catalogue the metabolite-protein interactions (MPIs) present in contexts, characterizing functional relevance MPIs remains a challenging problem. Computational approaches from constrained-based modeling framework allow for predicting and integrating their effects silico analysis metabolic physiological phenotypes, like cell growth. Here, we provide classification all existing constraint-based that predict integrate using genome-scale networks input. In addition, benchmark performance comparative study features extracted model structure predicted phenotypes state-of-the-art Escherichia coli Saccharomyces cerevisiae. Lastly, an outlook future, feasible directions expand consideration wide biotechnological applications.

Language: Английский

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