The role of metabolism in shaping enzyme structures over 400 million years
Journal article, 2025
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.
Author
Oliver Lemke
Charité University Medicine Berlin
Berliner Institut für Gesundheitsforschung
Benjamin M. Heineike
University of Oxford
The Francis Crick Institute
Charité University Medicine Berlin
Sandra Viknander
Chalmers, Life Sciences, Systems and Synthetic Biology
Nir Cohen
Charité University Medicine Berlin
Feiran Li
Chalmers, Life Sciences, Systems and Synthetic Biology
Jacob Lucas Steenwyk
University of California
Vanderbilt University
Leonard Spranger
Charité University Medicine Berlin
Federica Agostini
Charité University Medicine Berlin
Cory Thomas Lee
Charité University Medicine Berlin
Simran Kaur Aulakh
The Francis Crick Institute
University of Oxford
Judith Berman
Tel Aviv University
Antonis Rokas
Vanderbilt University
Jens B Nielsen
Chalmers, Life Sciences, Systems and Synthetic Biology
Toni I. Gossmann
Technische Universität Dortmund
Aleksej Zelezniak
King's College London
Vilnius University
Chalmers, Life Sciences, Systems and Synthetic Biology
M. Ralser
Max Planck Society
University of Oxford
Berliner Institut für Gesundheitsforschung
Charité University Medicine Berlin
The Francis Crick Institute
Nature
0028-0836 (ISSN) 1476-4687 (eISSN)
Vol. 644 8075 280-289Unlocking the Protein Production Potential of Non-Conventional Yeasts with Artificial Intelligence
Swedish Research Council (VR) (2023-04254), 2024-01-01 -- 2027-12-31.
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Formas (2019-01403), 2020-01-01 -- 2023-12-31.
Subject Categories (SSIF 2025)
Molecular Biology
Evolutionary Biology
Structural Biology
DOI
10.1038/s41586-025-09205-6