The topological properties of the protein universe
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....
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61108-2 |
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| _version_ | 1849343088477077504 |
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| author | Christian D. Madsen Agnese Barbensi Stephen Y. Zhang Lucy Ham Alessia David Douglas E. V. Pires Michael P. H. Stumpf |
| author_facet | Christian D. Madsen Agnese Barbensi Stephen Y. Zhang Lucy Ham Alessia David Douglas E. V. Pires Michael P. H. Stumpf |
| author_sort | Christian D. Madsen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b50faefb71824d83bcc90d6c1304eea5 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-b50faefb71824d83bcc90d6c1304eea52025-08-20T03:43:10ZengNature PortfolioNature Communications2041-17232025-08-0116111610.1038/s41467-025-61108-2The topological properties of the protein universeChristian D. Madsen0Agnese Barbensi1Stephen Y. Zhang2Lucy Ham3Alessia David4Douglas E. V. Pires5Michael P. H. Stumpf6School of Mathematics and Statistics, University of MelbourneSchool of Mathematics and Statistics, University of MelbourneSchool of Mathematics and Statistics, University of MelbourneSchool of Mathematics and Statistics, University of MelbourneDepartment of Life Sciences, Imperial CollegeSchool of Computing and Information Systems, University of MelbourneSchool of Mathematics and Statistics, University of MelbourneAbstract 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.https://doi.org/10.1038/s41467-025-61108-2 |
| spellingShingle | Christian D. Madsen Agnese Barbensi Stephen Y. Zhang Lucy Ham Alessia David Douglas E. V. Pires Michael P. H. Stumpf The topological properties of the protein universe Nature Communications |
| title | The topological properties of the protein universe |
| title_full | The topological properties of the protein universe |
| title_fullStr | The topological properties of the protein universe |
| title_full_unstemmed | The topological properties of the protein universe |
| title_short | The topological properties of the protein universe |
| title_sort | topological properties of the protein universe |
| url | https://doi.org/10.1038/s41467-025-61108-2 |
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