Author
Listed:
- Oliver Lemke
(Charité-Universitätsmedizin Berlin
Berlin Institute of Health at Charité)
- Benjamin Murray Heineike
(Charité-Universitätsmedizin Berlin
University of Oxford
The Francis Crick Institute)
- Sandra Viknander
(Chalmers University of Technology)
- Nir Cohen
(Charité-Universitätsmedizin Berlin)
- Feiran Li
(Chalmers University of Technology)
- Jacob Lucas Steenwyk
(University of California Berkeley
Vanderbilt University
University of California Berkeley
Vanderbilt University)
- Leonard Spranger
(Charité-Universitätsmedizin Berlin)
- Federica Agostini
(Charité-Universitätsmedizin Berlin)
- Cory Thomas Lee
(Charité-Universitätsmedizin Berlin)
- Simran Kaur Aulakh
(University of Oxford
The Francis Crick Institute)
- Judith Berman
(Tel Aviv University)
- Antonis Rokas
(Vanderbilt University
Vanderbilt University)
- Jens Nielsen
(Chalmers University of Technology)
- Toni Ingolf Gossmann
(TU Dortmund University)
- Aleksej Zelezniak
(Chalmers University of Technology
Vilnius University
Kingʼs College London)
- Markus Ralser
(Charité-Universitätsmedizin Berlin
Berlin Institute of Health at Charité
University of Oxford
The Francis Crick Institute)
Abstract
Advances in deep learning and AlphaFold2 have enabled the large-scale prediction of protein structures across species, opening avenues for studying protein function and evolution1. Here we analyse 11,269 predicted and experimentally determined enzyme structures that catalyse 361 metabolic reactions across 225 pathways to investigate metabolic evolution over 400 million years in the Saccharomycotina subphylum2. By linking sequence divergence in structurally conserved regions to a variety of metabolic properties of the enzymes, we reveal that metabolism shapes structural evolution across multiple scales, from species-wide metabolic specialization to network organization and the molecular properties of the enzymes. Although positively selected residues are distributed across various structural elements, enzyme evolution is constrained by reaction mechanisms, interactions with metal ions and inhibitors, metabolic flux variability and biosynthetic cost. Our findings uncover hierarchical patterns of structural evolution, in which structural context dictates amino acid substitution rates, with surface residues evolving most rapidly and small-molecule-binding sites evolving under selective constraints without cost optimization. By integrating structural biology with evolutionary genomics, we establish a model in which enzyme evolution is intrinsically governed by catalytic function and shaped by metabolic niche, network architecture, cost and molecular interactions.
Suggested Citation
Oliver Lemke & Benjamin Murray Heineike & Sandra Viknander & Nir Cohen & Feiran Li & Jacob Lucas Steenwyk & Leonard Spranger & Federica Agostini & Cory Thomas Lee & Simran Kaur Aulakh & Judith Berman , 2025.
"The role of metabolism in shaping enzyme structures over 400 million years,"
Nature, Nature, vol. 644(8075), pages 280-289, August.
Handle:
RePEc:nat:nature:v:644:y:2025:i:8075:d:10.1038_s41586-025-09205-6
DOI: 10.1038/s41586-025-09205-6
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