A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology
Abstract A major goal of cancer biology is to understand the mechanisms driven by somatically acquired mutations. Two distinct methodologies—one analyzing mutation clustering within protein sequences and 3D structures, the other leveraging protein-protein interaction network topology—offer complemen...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-54176-3 |
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| _version_ | 1832585545415393280 |
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| author | Yingying Zhang Alden K. Leung Jin Joo Kang Yu Sun Guanxi Wu Le Li Jiayang Sun Lily Cheng Tian Qiu Junke Zhang Shayne D. Wierbowski Shagun Gupta James G. Booth Haiyuan Yu |
| author_facet | Yingying Zhang Alden K. Leung Jin Joo Kang Yu Sun Guanxi Wu Le Li Jiayang Sun Lily Cheng Tian Qiu Junke Zhang Shayne D. Wierbowski Shagun Gupta James G. Booth Haiyuan Yu |
| author_sort | Yingying Zhang |
| collection | DOAJ |
| description | Abstract A major goal of cancer biology is to understand the mechanisms driven by somatically acquired mutations. Two distinct methodologies—one analyzing mutation clustering within protein sequences and 3D structures, the other leveraging protein-protein interaction network topology—offer complementary strengths. We present NetFlow3D, a unified, end-to-end 3D structurally-informed protein interaction network propagation framework that maps the multiscale mechanistic effects of mutations. Built upon the Human Protein Structurome, which incorporates the 3D structures of every protein and the binding interfaces of all known protein interactions, NetFlow3D integrates atomic, residue, protein and network-level information: It clusters mutations on 3D protein structures to identify driver mutations and propagates their impacts anisotropically across the protein interaction network, guided by the involved interaction interfaces, to reveal systems-level impacts. Applied to 33 cancer types, NetFlow3D identifies 2 times more 3D clusters and incorporates 8 times more proteins in significantly interconnected network modules compared to traditional methods. |
| format | Article |
| id | doaj-art-110b4d63ca1b4260896324d74a34f09c |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-110b4d63ca1b4260896324d74a34f09c2025-01-26T12:40:52ZengNature PortfolioNature Communications2041-17232025-01-0116111810.1038/s41467-024-54176-3A multiscale functional map of somatic mutations in cancer integrating protein structure and network topologyYingying Zhang0Alden K. Leung1Jin Joo Kang2Yu Sun3Guanxi Wu4Le Li5Jiayang Sun6Lily Cheng7Tian Qiu8Junke Zhang9Shayne D. Wierbowski10Shagun Gupta11James G. Booth12Haiyuan Yu13Department of Computational Biology, Cornell UniversityDepartment of Computational Biology, Cornell UniversityDepartment of Computational Biology, Cornell UniversityDepartment of Computational Biology, Cornell UniversityCollege of Agriculture and Life Sciences, Cornell UniversityDepartment of Computational Biology, Cornell UniversityDepartment of Computational Biology, Cornell UniversityDepartment of Science and Technology Studies, Cornell UniversitySchool of Electrical and Computer Engineering, Cornell UniversityDepartment of Computational Biology, Cornell UniversityDepartment of Computational Biology, Cornell UniversityDepartment of Computational Biology, Cornell UniversityDepartment of Computational Biology, Cornell UniversityDepartment of Computational Biology, Cornell UniversityAbstract A major goal of cancer biology is to understand the mechanisms driven by somatically acquired mutations. Two distinct methodologies—one analyzing mutation clustering within protein sequences and 3D structures, the other leveraging protein-protein interaction network topology—offer complementary strengths. We present NetFlow3D, a unified, end-to-end 3D structurally-informed protein interaction network propagation framework that maps the multiscale mechanistic effects of mutations. Built upon the Human Protein Structurome, which incorporates the 3D structures of every protein and the binding interfaces of all known protein interactions, NetFlow3D integrates atomic, residue, protein and network-level information: It clusters mutations on 3D protein structures to identify driver mutations and propagates their impacts anisotropically across the protein interaction network, guided by the involved interaction interfaces, to reveal systems-level impacts. Applied to 33 cancer types, NetFlow3D identifies 2 times more 3D clusters and incorporates 8 times more proteins in significantly interconnected network modules compared to traditional methods.https://doi.org/10.1038/s41467-024-54176-3 |
| spellingShingle | Yingying Zhang Alden K. Leung Jin Joo Kang Yu Sun Guanxi Wu Le Li Jiayang Sun Lily Cheng Tian Qiu Junke Zhang Shayne D. Wierbowski Shagun Gupta James G. Booth Haiyuan Yu A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology Nature Communications |
| title | A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology |
| title_full | A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology |
| title_fullStr | A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology |
| title_full_unstemmed | A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology |
| title_short | A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology |
| title_sort | multiscale functional map of somatic mutations in cancer integrating protein structure and network topology |
| url | https://doi.org/10.1038/s41467-024-54176-3 |
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