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Journal Article

AI-based structure prediction empowers integrative structural analysis of human nuclear pores

MPS-Authors
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Mosalaganti,  Shyamal
Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society;
Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany;
Life Sciences Institute and Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, USA;

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Obarska-Kosinska,  Agnieszka
Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society;
European Molecular Biology Laboratory Hamburg, Hamburg, Germany;

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Siggel,  Marc
European Molecular Biology Laboratory Hamburg, Hamburg, Germany;
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;
Centre for Structural Systems Biology, Hamburg, Germany;

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Taniguchi,  Reiya
Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society;
Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany;

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Turoňová,  Beata
Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society;
Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany;

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Zimmerli,  Christian E.
Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society;
Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany;

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Margiotta,  Erica
Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society;
Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany;

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Hummer,  Gerhard       
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;
Institute of Biophysics, Goethe University Frankfurt, Frankfurt am Main, Germany;

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Beck,  Martin       
Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society;
Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany;

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Citation

Mosalaganti, S., Obarska-Kosinska, A., Siggel, M., Taniguchi, R., Turoňová, B., Zimmerli, C. E., et al. (2022). AI-based structure prediction empowers integrative structural analysis of human nuclear pores. Science, 376(6598): eabm9506. doi:10.1126/science.abm9506.


Cite as: https://hdl.handle.net/21.11116/0000-000A-9651-1
Abstract
Nuclear pore complexes (NPCs) mediate nucleocytoplasmic transport. Their intricate 120-megadalton architecture remains incompletely understood. Here, we report a 70-megadalton model of the human
NPC scaffold with explicit membrane and in multiple conformational states. We combined artificial intelligence (AI)–based structure prediction with in situ and in cellulo cryo–electron tomography and integrative modeling. We show that linker nucleoporins spatially organize the scaffold within and across subcomplexes to establish the higher-order structure. Microsecond-long molecular dynamics simulations
suggest that the scaffold is not required to stabilize the inner and outer nuclear membrane fusion but rather widens the central pore. Our work exemplifies how AI-based modeling can be integrated with
in situ structural biology to understand subcellular architecture across spatial organization levels.