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The Role of Metabolism in Shaping Enzyme Structures Over 400 Million Years of Evolution
Open accessPreprint — published as 10.1038/s41586-025-09205-6
Abstract
The functions of cells and proteins depend on their biochemical microenvironment. To understand how biochemical constraints shaped protein structural evolution, we coupled the extensive genetic and metabolic data from the Saccharomycotina subphylum with the capability of AlphaFold2 to systematically predict protein structures from sequence. Determining how 11,269 enzyme structures catalysing 361 different metabolic reactions evolved over 400 million years alongside their molecular functions, we report that metabolism has shaped the structural evolution of enzymes at different levels: the organism’s overall metabolism; the topological organisation of the metabolic network; and each enzyme’s molecular properties. For example, structural evolution depends on each enzyme’s reaction mechanism, on the variability rather than the amount of metabolic flux, and on biosynthetic cost. Evolutionary cost-optimization is stronger on highly abundant enzymes and acts differently on different structural domains, with the exception of small-molecule binding sites, which are prioritised over other structural domains and lack cost-optimisation. Finally, while enzyme surfaces are less constrained, surface residues can also be exposed to positive selection for the co-evolution of protein-protein interaction sites. Accessing AlphaFold’s power to predict protein structures systematically and across species barriers, facilitating the integration of protein structures with functional genomics, we were thus able to map biological constraints which shape protein structural evolution at scale and over long timelines.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
A structural biology community assessment of AlphaFold2 applications
Akdel, M., Pires, D. E. V., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00849-w · Cited by 675
AlphaFold and Implications for Intrinsically Disordered Proteins
Ruff, K. M., & Pappu, R. V. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167208 · Cited by 635
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
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Metadata
- Authors
- Lemke, O., Heineike, B. M., Viknander, S., Cohen, N., Steenwyk, J. L., Spranger, L., Li, F., Agostini, F., Lee, C. T., Aulakh, S. K., Nielsen, J., Rokas, A., Berman, J., Zelezniak, A., Gossmann, T. I., & Ralser, M.
- Affiliations
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Centre for Human Genetics (GB)
- Chalmers University of Technology (SE)
- Charité – Universitätsmedizin Berlin (DE)
- Howard Hughes Medical Institute (US)
- Vanderbilt University (US)
- Tel Aviv University (IL)
- Vilnius University (LT)
- TU Dortmund University (DE)
- Corresponding author
- Ralser, Markus
- ORCID
- Venue
- Nature
- Publisher
- openRxiv
- Published
- 2024-05-27
- Volume
- 644
- Issue
- 8075
- Pages
- 280-289
- ISSN
- 0028-0836 (ISSN-L)
- PMC
- PMC12328220
- OpenAlex
- W4412151053
- Cited by
- 28
- References
- 4
- Citations by year
- 2026: 17 · 2025: 9 · 2024: 2
- FWCI
- 7.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Nature (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion, cc-by)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Funders
- Wellcome Trust (200829/Z/16/Z)
- Biotechnology and Biological Sciences Research Council (BB/Y000730/1)
- National Institute of Allergy and Infectious Diseases (R01AI153356)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- enzyme evolution, metabolic evolution, protein structure, Saccharomycotina, metabolic pathways, positive selection, amino acid substitution rates, metal ion binding, enzyme inhibitors, small molecule binding sites, reaction mechanism, deep learning protein structure prediction
- MeSH terms
- Binding Sites, Enzymes, Models, Molecular, Phylogeny, Protein Conformation, Selection, Genetic, Evolution, Molecular, Amino Acid Substitution, Metabolic Networks and Pathways, Biocatalysis
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