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Network-level architecture and the evolutionary potential of underground metabolism
Open accessAbstract
Significance: Understanding how new metabolic pathways emerge is one of the key issues in evolutionary and systems biology. The prevailing paradigm is that evolution capitalizes on the weak side activities of preexisting enzymes (i.e. underground reactions). However, the extent to which underground reactions provide novelties in the context of the entire cellular system has remained unexplored. In this study, we present a comprehensive computational model of the underground metabolism of Escherichia coli . Together with a high-throughput experimental survey across hundreds of nutrient environments we predicted and confirmed new functional states of metabolism in which underground reactions allow growth when their activity is increased. Our approach has important implications for biotechnological and medical applications, such as understanding gain-of-function mutations in tumor development.
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DLKcat could differentiate between native (top 10% experimental kcat values, median = 2.22 s-1) and underground (last 10%, median = 0.04 s-1) substrates for human aldo-keto reductase and 61 substrates (P = 0.0039).
“The concept of native and underground metabolism 24 could be exemplified with the rich experimental k cat data that are available for human aldo-keto reductase and 61 substrates, where DLKcat could differentiate (Fig. 2d; P = 0.0039) between native (top 10% experimental k cat values, median = 2.22 s -1 ) and underground (last 10%, median = 0.04 s -1 ) substrates.”
From Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction (2022) · cites this paper as [24]
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Sorted by citations
Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction
Li, F., Yuan, L., Lu, H., Li, G., Chen, Y., Engqvist, M. K. M., Kerkhoven, E. J., & Nielsen, J. (2022) · Nature Catalysis · doi:10.1038/s41929-022-00798-z · Cited by 466
“The concept of native and underground metabolism 24 could be exemplified with the rich experimental k cat data that are available for human aldo–keto reductase and 61 substrates, where DLKcat could differentiate (Fig.”
BackgroundMethodMachado, D., Herrgård, M. J., & Rocha, I. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005140 · Cited by 82
Lessons on enzyme kinetics from quantitative proteomics
Davidi, D., & Milo, R. (2017) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2017.02.007 · Cited by 72
Metadata
- Authors
- Notebaart, R. A., Szappanos, B., Kintses, B., Pál, F., Györkei, Á., Bogos, B., Lázár, V., Spohn, R., Csörgő, B., Wagner, A., Ruppin, E., Pál, C., & Papp, B.
- Affiliations
- Radboud University Nijmegen (NL)
- HUN-REN Szegedi Biológiai Kutatóközpont (HU)
- The Blavatnik School of Computer Science and
- Tel Aviv University (IL)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2014-07-28
- Volume
- 111
- Issue
- 32
- Pages
- 11762-11767
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4136603
- OpenAlex
- W2124962680
- Cited by
- 123
- References
- 3
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 10 · 2023: 4 · 2022: 8 · 2021: 10 · 2020: 10 · 2019: 10 · 2018: 14 · 2017: 10 · 2016: 15 · 2015: 17 · 2014: 3
- FWCI
- 6.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- SZTE Publicatio Repozitórium (University of Szeged) (acceptedVersion, other-oa)
- Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) (acceptedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Wellcome Trust (098016/Z/11/Z, 098016)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- Escherichia coli, metabolic innovation, metabolic pathway reconstruction, in silico metabolic modeling, adaptive potential, adaptation to novel environments, fitness prediction
- MeSH terms
- Adaptation, Physiological, Computer Simulation, Enzymes, Biological Evolution, Models, Biological, Phenotype, Genome, Bacterial, Escherichia coli Proteins, Escherichia coli K12, Metabolic Networks and Pathways
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