Network Context and Selection in the Evolution to Enzyme Specificity
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Good Enough Can Be Good Enough: To begin to understand why some enzymes are promiscuous and have many substrates, whereas others are highly specific, and why some have high activity, whereas others appear not to be optimized, Nam et al. (p. 1101 ) analyzed metabolic networks in bacteria. Specialist enzymes are essential for life, catalyze a high flux of enzymatic activity, and are more highly regulated. However, not all enzymes appear to be on a track of gradual improvement of specificity and efficiency. Generalist enzymes seem to well serve their own purposes, and their optimization may not justify the evolutionary cost.
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This specialist-generalist kcat trend is aligned with the hypothesis that ancestral enzymes with broad substrate specificity and low catalytic efficiency improve their kcat value when they evolve into specialists through mutation, gene duplication or horizontal gene transfer.
“This is aligned with the hypothesis that ancestral enzymes with broad substrate specificity and low catalytic efficiency improve their k cat value when they evolve into specialists through mutation, gene duplication or horizontal gene transfer 29 .”
From Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction (2022) · cites this paper as [29]
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Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction
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“This is aligned with the hypothesis that ancestral enzymes with broad substrate specificity and low catalytic efficiency improve their k cat value when they evolve into specialists through mutation, gene duplication or horizontal gene transfer 29 .”
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- Authors
- Nam, H., Lewis, N. E., Lerman, J. A., Lee, D.-H., Chang, R. L., Kim, D., & Palsson, B. O.
- Affiliations
- University of California San Diego (US)
- Harvard University (US)
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093–0412, USA.
- Venue
- Science
- Published
- 2012-08-31
- Volume
- 337
- Issue
- 6098
- Pages
- 1101-1104
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC3536066
- OpenAlex
- W2068855150
- Cited by
- 277
- References
- 38
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 9 · 2023: 13 · 2022: 21 · 2021: 19 · 2020: 18 · 2019: 23 · 2018: 24 · 2017: 14 · 2016: 25 · 2015: 29 · 2014: 27 · 2013: 28 · 2012: 7
- FWCI
- 11.69
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM057089)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- enzyme specificity, enzyme evolution, substrate promiscuity, metabolic flux, enzyme regulation, essential enzymes, Escherichia coli metabolism, genome-scale metabolic models, archaea, eukaryotes
- MeSH terms
- Catalysis, Enzymes, Escherichia coli, Selection, Genetic, Substrate Specificity, Evolution, Molecular, Computational Biology, Metabolic Networks and Pathways
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