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蛋白质世界的拓扑性质。

The topological properties of the protein universe.

作者信息

Madsen Christian D, Barbensi Agnese, Zhang Stephen Y, Ham Lucy, David Alessia, Pires Douglas E V, Stumpf Michael P H

机构信息

School of Mathematics and Statistics, University of Melbourne, Parkville, Australia.

Melbourne Integrative Genomics, University of Melbourne, Parkville, Australia.

出版信息

Nat Commun. 2025 Aug 13;16(1):7503. doi: 10.1038/s41467-025-61108-2.

DOI:10.1038/s41467-025-61108-2
PMID:40804041
Abstract

Deep learning methods have revolutionised our ability to predict protein structures, allowing us a glimpse into the entire protein universe. As a result, our understanding of how protein structure drives function is now lagging behind our ability to determine and predict protein structure. Here, we describe how topology, the branch of mathematics concerned with qualitative properties of spatial structures, provides a lens through which we can identify fundamental organising features across the known protein universe. We identify topological determinants that capture global features of the protein universe, such as domain architecture and binding sites. Additionally, our analysis identifies highly specific properties, so-called topological generators, that can be used to provide deeper insights into protein structure-function and evolutionary relationships. We present a practical methodology for mapping the topology of the known protein universe at scale. We then use our approach to determine structural, functional and disease consequences of mutations. Our approach reveals and helps to explain differences in properties of proteins in mesophiles and thermophiles, and the likely structural and functional consequences of polymorphisms in a protein. For eukaryotes we find striking differences between protein topologies in multi-cellular and single-celled organisms.

摘要

深度学习方法彻底改变了我们预测蛋白质结构的能力,使我们得以一窥整个蛋白质世界。因此,我们目前对蛋白质结构如何驱动功能的理解,滞后于我们确定和预测蛋白质结构的能力。在此,我们描述了拓扑学(数学中关注空间结构定性性质的分支)如何提供一个视角,通过它我们能够识别已知蛋白质世界中的基本组织特征。我们确定了能够捕捉蛋白质世界全局特征(如结构域架构和结合位点)的拓扑决定因素。此外,我们的分析还识别出了高度特异性的性质,即所谓的拓扑生成元,它们可用于更深入地洞察蛋白质的结构 - 功能以及进化关系。我们提出了一种在大规模上绘制已知蛋白质世界拓扑结构的实用方法。然后,我们运用我们的方法来确定突变的结构、功能和疾病后果。我们的方法揭示并有助于解释嗜温菌和嗜热菌中蛋白质性质的差异,以及蛋白质中多态性可能产生的结构和功能后果。对于真核生物,我们发现多细胞生物和单细胞生物的蛋白质拓扑结构存在显著差异。

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本文引用的文献

1
AlphaFold Blindness to Topological Barriers Affects Its Ability to Correctly Predict Proteins' Topology.AlphaFold 对拓扑障碍的盲目性影响其正确预测蛋白质拓扑结构的能力。
Molecules. 2023 Nov 7;28(22):7462. doi: 10.3390/molecules28227462.
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Clustering predicted structures at the scale of the known protein universe.对已知蛋白质宇宙尺度的预测结构进行聚类。
Nature. 2023 Oct;622(7983):637-645. doi: 10.1038/s41586-023-06510-w. Epub 2023 Sep 13.
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Uncovering new families and folds in the natural protein universe.揭示自然蛋白质宇宙中的新家族和新折叠。
Nature. 2023 Oct;622(7983):646-653. doi: 10.1038/s41586-023-06622-3. Epub 2023 Sep 13.
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Homology of homologous knotted proteins.同源打结蛋白的同源性。
J R Soc Interface. 2023 Apr;20(201):20220727. doi: 10.1098/rsif.2022.0727. Epub 2023 Apr 26.
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Evolutionary-scale prediction of atomic-level protein structure with a language model.用语言模型进行原子级蛋白质结构的进化尺度预测。
Science. 2023 Mar 17;379(6637):1123-1130. doi: 10.1126/science.ade2574. Epub 2023 Mar 16.
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AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms.AlphaFold2 揭示了 21 个模式生物的蛋白质结构空间中的共性和新颖性。
Commun Biol. 2023 Feb 8;6(1):160. doi: 10.1038/s42003-023-04488-9.
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UniProt: the Universal Protein Knowledgebase in 2023.UniProt:2023 年的通用蛋白质知识库。
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10
Topological approximate Bayesian computation for parameter inference of an angiogenesis model.拓扑近似贝叶斯计算在血管生成模型参数推断中的应用。
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