德国柏林夏里特大学Markus Ralser团队在研究中取得进展。他们的研究开发出了4亿年来代谢在形成酶结构中的作用。该研究于2025年7月9日发表于国际一流学术期刊《自然》杂志上。

在这里，研究人员分析了11269种预测和实验确定的酶结构，这些酶结构催化225种途径的361种代谢反应，以研究酵母亚门4亿年的代谢进化。通过将结构保守区域的序列分化与酶的各种代谢特性联系起来，课题组揭示了代谢在多个尺度上塑造了结构进化，从物种范围的代谢特化到网络组织和酶的分子特性。虽然正选择残基分布在各种结构元件中，但酶的进化受到反应机制、与金属离子和抑制剂的相互作用、代谢通量可变性和生物合成成本的限制。

他们的发现揭示了结构进化的层次模式，其中结构背景决定了氨基酸取代率，表面残基进化最快，小分子结合位点在没有成本优化的选择约束下进化。通过将结构生物学与进化基因组学相结合，课题组研究人员建立了一个模型，在这个模型中，酶的进化本质上是由催化功能控制的，并由代谢生态位、网络结构、成本和分子相互作用形成。

据介绍，深度学习和AlphaFold2的进步使跨物种蛋白质结构的大规模预测成为可能，为研究蛋白质功能和进化开辟了途径。

附：英文原文

Title: The role of metabolism in shaping enzyme structures over 400 million years

Author: Lemke, Oliver, Heineike, Benjamin Murray, Viknander, Sandra, Cohen, Nir, Li, Feiran, Steenwyk, Jacob Lucas, Spranger, Leonard, Agostini, Federica, Lee, Cory Thomas, Aulakh, Simran Kaur, Berman, Judith, Rokas, Antonis, Nielsen, Jens, Gossmann, Toni Ingolf, Zelezniak, Aleksej, Ralser, Markus

Issue&Volume: 2025-07-09

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.

DOI: 10.1038/s41586-025-09205-6

Source: https://www.nature.com/articles/s41586-025-09205-6