Cited by 14Protein Structure and DynamicsRNA Research and SplicingMicrotubule and mitosis dynamics

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The filamentous ultrastructure of the PopZ condensate is required for its cellular function

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Abstract

Abstract Biomolecular condensates play key roles in the spatiotemporal regulation of cellular processes. Yet, the relationship between atomic features and condensate function remains poorly understood. We studied this relationship using the polar organizing protein Z (PopZ) as a model system, revealing how its material properties and cellular function depend on its ultrastructure. We revealed PopZ’s hierarchical assembly into a filamentous condensate by integrating cryo-electron tomography, biochemistry, single-molecule techniques, and molecular dynamics simulations. The helical domain drives filamentation and condensation, while the disordered domain inhibits them. Phase-dependent conformational changes prevent interfilament contacts in the dilute phase and expose client binding sites in the dense phase. These findings establish a multiscale framework that links molecular interactions and condensate ultrastructure to macroscopic material properties that drive cellular function.

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  1. Banani, S. F., Lee, H. O., Hyman, A. A. & Rosen, M. K. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18, 285–298 (2017). · DOI
  2. Shin, Y. & Brangwynne, C. P. Liquid phase condensation in cell physiology and disease. Science 357, eaaf4382 (2017). · DOI
  3. Boeynaems, S. et al. Phase separation in biology and disease; current perspectives and open questions. J. Mol. Biol. 435, 167971 (2023). · DOI
  4. Choi, J. M., Holehouse, A. S. & Pappu, R. V. Physical principles underlying the complex biology of intracellular phase transitions. Annu. Rev. Biophys. 49, 107–133 (2020). · DOI
  5. Pei, G., Lyons, H., Li, P. & Sabari, B. R. Transcription regulation by biomolecular condensates. Nat. Rev. Mol. Cell Biol. 26, 213–236 (2025). · DOI
  6. Su, X. et al. Phase separation of signaling molecules promotes T cell receptor signal transduction. Science 352, 595–599 (2016). · DOI
  7. Alberti, S. & Hyman, A. A. Biomolecular condensates at the nexus of cellular stress, protein aggregation disease and ageing. Nat. Rev. Mol. Cell Biol. 22, 196–213 (2021). · DOI
  8. Visser, B. S., Lipinski, W. P. & Spruijt, E. The role of biomolecular condensates in protein aggregation. Nat. Rev. Chem. 8, 686–700 (2024). · DOI
  9. Pappu, R. V., Cohen, S. R., Dar, F., Farag, M. & Kar, M. Phase transitions of associative biomacromolecules. Chem. Rev. 123, 8945–8987 (2023). · DOI
  10. Mittag, T. & Pappu, R. V. A conceptual framework for understanding phase separation and addressing open questions and challenges. Mol. Cell 82, 2201–2214 (2022). · DOI
  11. Gouveia, B. et al. Capillary forces generated by biomolecular condensates. Nature 609, 255–264 (2022). · DOI
  12. Alshareedah, I. et al. Sequence-specific interactions determine viscoelasticity and ageing dynamics of protein condensates. Nat. Phys. 20, 1482–1491 (2024). · DOI
  13. Feric, M. et al. Coexisting liquid phases underlie nucleolar subcompartments. Cell 165, 1686–1697 (2016). · DOI
  14. Posey, A. E. et al. Biomolecular condensates are characterized by interphase electric potentials. J. Am. Chem. Soc. 146, 28268–28281 (2024). · DOI
  15. Peran, I. & Mittag, T. Molecular structure in biomolecular condensates. Curr. Opin. Struct. Biol. 60, 17–26 (2020). · DOI
  16. Bienz, M. Head-to-tail polymerization in the assembly of biomolecular condensates. Cell 182, 799–811 (2020). · DOI
  17. Fletcher, D. A. & Mullins, R. D. Cell mechanics and the cytoskeleton. Nature 463, 485–492 (2010). · DOI
  18. Berkamp, S., Mostafavi, S. & Sachse, C. Structure and function of p62/SQSTM1 in the emerging framework of phase separation. FEBS J. 288, 6927–6941 (2021). · DOI
  19. Bouchard, J. J. et al. Cancer mutations of the tumor suppressor SPOP disrupt the formation of active, phase-separated compartments. Mol Cell 72, 19–36 e8 (2018). · DOI
  20. Weirich, K. L. et al. Liquid behavior of cross-linked actin bundles. Proc. Natl Acad. Sci. USA 114, 2131–2136 (2017). · DOI
  21. Bowman, G. R. et al. A polymeric protein anchors the chromosomal origin/ParB complex at a bacterial cell pole. Cell 134, 945–955 (2008). · DOI
  22. Ebersbach, G., Briegel, A., Jensen, G. J. & Jacobs-Wagner, C. A self-associating protein critical for chromosome attachment, division, and polar organization in Caulobacter. Cell 134, 956–968 (2008). · DOI
  23. Bowman, G. R. et al. Caulobacter PopZ forms a polar subdomain dictating sequential changes in pole composition and function. Mol. Microbiol. 76, 173–189 (2010). · DOI
  24. Ettema, T. J. & Andersson, S. G. The α-proteobacteria: the Darwin finches of the bacterial world. Biol. Lett. 5, 429–432 (2009). · DOI
  25. Tsokos, C. G. & Laub, M. T. Polarity and cell fate asymmetry in Caulobacter crescentus. Curr. Opin. Microbiol. 15, 744–750 (2012). · DOI
  26. Govers, S. K. & Jacobs-Wagner, C. Caulobacter crescentus: model system extraordinaire. Curr. Biol. 30, R1151–R1158 (2020). · DOI
  27. Lasker, K., Mann, T. H. & Shapiro, L. An intracellular compass spatially coordinates cell cycle modules in Caulobacter crescentus. Curr. Opin. Microbiol. 33, 131–139 (2016). · DOI
  28. Lasker, K. et al. Selective sequestration of signalling proteins in a membraneless organelle reinforces the spatial regulation of asymmetry in Caulobacter crescentus. Nat. Microbiol. 5, 418–429 (2020). · DOI
  29. Lasker, K. et al. The material properties of a bacterial-derived biomolecular condensate tune biological function in natural and synthetic systems. Nat. Commun. 13, 5643 (2022). · DOI
  30. Heebner, J. E. et al. Deep learning-based segmentation of cryo-electron tomograms. J. Vis. Exp. (189), e64435 (2022). · DOI
  31. Oosawa, F. & Kasai, M. A theory of linear and helical aggregations of macromolecules. J. Mol. Biol. 4, 10–21 (1962). · DOI
  32. Romberg, L., Simon, M. & Erickson, H. P. Polymerization of Ftsz, a bacterial homolog of tubulin. Is assembly cooperative? J. Biol. Chem. 276, 11743–11753 (2001). · DOI
  33. Toro-Nahuelpan, M., Plitzko, J. M., Schüler, D. & Pfeiffer, D. In vivo architecture of the polar organizing protein Z (PopZ) meshwork in the alphaproteobacteria Magnetospirillum gryphiswaldense and Caulobacter crescentus. J. Mol. Biol. 434, 167423 (2022). · DOI
  34. Bowman, G. R. et al. Oligomerization and higher-order assembly contribute to sub-cellular localization of a bacterial scaffold. Mol. Microbiol. 90, 776–795 (2013). · DOI
  35. Laloux, G. & Jacobs-Wagner, C. Spatiotemporal control of PopZ localization through cell cycle-coupled multimerization. J. Cell Biol. 201, 827–841 (2013). · DOI
  36. Holmes, J. A. et al. Caulobacter PopZ forms an intrinsically disordered hub in organizing bacterial cell poles. Proc. Natl Acad. Sci. USA 113, 12490–12495 (2016). · DOI
  37. Ptacin, J. L. et al. Bacterial scaffold directs pole-specific centromere segregation. Proc. Natl Acad. Sci. USA 111, E2046–E2055 (2014). · DOI
  38. Marzahn, M. R. et al. Higher-order oligomerization promotes localization of SPOP to liquid nuclear speckles. EMBO J. 35, 1254–1275 (2016). · DOI
  39. Soltermann, F. et al. Quantifying protein–protein interactions by molecular counting with mass photometry. Angew. Chem. Int. Ed. Engl. 59, 10774–10779 (2020). · DOI
  40. Young, G. et al. Quantitative mass imaging of single biological macromolecules. Science 360, 423–427 (2018). · DOI
  41. Howlett, G. J., Minton, A. P. & Rivas, G. Analytical ultracentrifugation for the study of protein association and assembly. Curr. Opin. Chem. Biol. 10, 430–436 (2006). · DOI
  42. Agudo-Canalejo, J. et al. Wetting regulates autophagy of phase-separated compartments and the cytosol. Nature 591, 142–146 (2021). · DOI
  43. Alshareedah, I., Kaur, T. & Banerjee, P. R. Methods for characterizing the material properties of biomolecular condensates. Methods Enzymol. 646, 143–183 (2021). · DOI
  44. Ditlev, J. A., Case, L. B. & Rosen, M. K. Who’s in and who’s out—compositional control of biomolecular condensates. J. Mol. Biol. 430, 4666–4684 (2018). · DOI
  45. Nordyke, C. T., Ahmed, Y., Puterbaugh, R. Z., Bowman, G. R. & Varga, K. Intrinsically disordered bacterial polar organizing protein Z, PopZ, interacts with protein binding partners through an N-terminal molecular recognition feature. J. Mol. Biol. 432, 6092–6107 (2020). · DOI
  46. Biondi, E. G. et al. Regulation of the bacterial cell cycle by an integrated genetic circuit. Nature 444, 899–904 (2006). · DOI
  47. Scholl, D. & Deniz, A. A. Conformational freedom and topological confinement of proteins in biomolecular condensates. J. Mol. Biol. 434, 167348 (2022). · DOI
  48. Saar, K. L. et al. Theoretical and data-driven approaches for biomolecular condensates. Chem. Rev. 123, 8988–9009 (2023). · DOI
  49. Latham, A. P. & Zhang, B. Unifying coarse-grained force fields for folded and disordered proteins. Curr. Opin. Struct. Biol. 72, 63–70 (2022). · DOI
  50. Bentley, E. P., Scholl, D., Wright, P. E. & Deniz, A. A. Coupling of binding and differential subdomain folding of the intrinsically disordered transcription factor CREB. FEBS Lett. 597, 917–932 (2023). · DOI
  51. Lerner, E. et al. FRET-based dynamic structural biology: challenges, perspectives and an appeal for open-science practices. eLife 10, e60416 (2021). · DOI
  52. Fitzkee, N. C. & Rose, G. D. Reassessing random-coil statistics in unfolded proteins. Proc. Natl Acad. Sci. USA 101, 12497–12502 (2004). · DOI
  53. Lotthammer, J. M., Ginell, G. M., Griffith, D., Emenecker, R. J. & Holehouse, A. S. Direct prediction of intrinsically disordered protein conformational properties from sequence. Nat. Methods 21, 465–476 (2024). · DOI
  54. Das, R. K., Ruff, K. M. & Pappu, R. V. Relating sequence encoded information to form and function of intrinsically disordered proteins. Curr. Opin. Struct. Biol. 32, 102–112 (2015). · DOI
  55. Antonik, M., Felekyan, S., Gaiduk, A. & Seidel, C. A. Separating structural heterogeneities from stochastic variations in fluorescence resonance energy transfer distributions via photon distribution analysis. J. Phys. Chem. B 110, 6970–6978 (2006). · DOI
  56. Livny, J., Yamaichi, Y. & Waldor, M. K. Distribution of centromere-like parS sites in bacteria: insights from comparative genomics. J. Bacteriol. 189, 8693–8703 (2007). · DOI
  57. Ptacin, J. L. et al. A spindle-like apparatus guides bacterial chromosome segregation. Nat. Cell Biol. 12, 791–798 (2010). · DOI
  58. Shebelut, C. W., Guberman, J. M., van Teeffelen, S., Yakhnina, A. A. & Gitai, Z. Caulobacter chromosome segregation is an ordered multistep process. Proc. Natl Acad. Sci. USA 107, 14194–14198 (2010). · DOI
  59. Puentes-Rodriguez, S. G., Norcross, J. D. & Mera, P. E. To let go or not to let go: how ParA can impact the release of the chromosomal anchoring in Caulobacter crescentus. Nucleic Acids Res. 51, 12275–12287 (2023). · DOI
  60. Tsai, J. W. & Alley, M. R. Proteolysis of the Caulobacter McpA chemoreceptor is cell cycle regulated by a ClpX-dependent pathway. J. Bacteriol. 183, 5001–5007 (2001). · DOI
  61. Mastronarde, D. N. Automated electron microscope tomography using robust prediction of specimen movements. J. Struct. Biol. 152, 36–51 (2005). · DOI
  62. Eisenstein, F. et al. Parallel cryo electron tomography on in situ lamellae. Nat. Methods 20, 131–138 (2023). · DOI
  63. Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017). · DOI
  64. Buchholz, T. O. et al. Content-aware image restoration for electron microscopy. Methods Cell. Biol. 152, 277–289 (2019). · DOI
  65. Liu, Y. T. et al. Isotropic reconstruction for electron tomography with deep learning. Nat. Commun. 13, 6482 (2022).
  66. Chen, M. et al. A complete data processing workflow for cryo-ET and subtomogram averaging. Nat. Methods 16, 1161–1168 (2019). · DOI
  67. Winkler, H. 3D reconstruction and processing of volumetric data in cryo-electron tomography. J. Struct. Biol. 157, 126–137 (2007). · DOI
  68. Zhang, K. Gctf: real-time CTF determination and correction determination and correction. J. Struct. Biol. 193, 1–12 (2016). · DOI
  69. Morado, D. R., Hu, B. & Liu, J. Using Tomoauto: a protocol for high-throughput automated cryo-electron tomography. J. Vis. Exp. (107), e53608 (2016). · DOI
  70. Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017). · DOI · Paper page
  71. Rohou, A. & Grigorieff, N. CTFFIND4: fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015). · DOI
  72. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012). · DOI · Paper page
  73. Ducret, A., Quardokus, E. M. & Brun, Y. V. MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis. Nat. Microbiol. 1, 16077 (2016). · DOI
  74. Schuck, P. Size-distribution analysis of macromolecules by sedimentation velocity ultracentrifugation and lamm equation modeling. Biophys. J. 78, 1606–1619 (2000). · DOI
  75. Philo, J. S. SEDNTERP: a calculation and database utility to aid interpretation of analytical ultracentrifugation and light scattering data. Eur. Biophys. J. 52, 233–266 (2023). · DOI
  76. Clementi, C., Nymeyer, H. & Onuchic, J. N. Topological and energetic factors: what determines the structural details of the transition state ensemble and ‘en-route’ intermediates for protein folding? An investigation for small globular proteins. J. Mol. Biol. 298, 937–953 (2000). · DOI
  77. Latham, A. P. & Zhang, B. Consistent force field captures homologue-resolved HP1 phase separation. J. Chem. Theory Comput. 17, 3134–3144 (2021). · DOI
  78. Regy, R. M., Thompson, J., Kim, Y. C. & Mittal, J. Improved coarse-grained model for studying sequence dependent phase separation of disordered proteins. Protein Sci. 30, 1371–1379 (2021). · DOI
  79. Liu, S., Wang, C., Latham, A. P., Ding, X. & Zhang, B. OpenABC enables flexible, simplified, and efficient GPU accelerated simulations of biomolecular condensates. PLoS Comput. Biol. 19, e1011442 (2023).
  80. Dignon, G. L., Zheng, W., Best, R. B., Kim, Y. C. & Mittal, J. Relation between single-molecule properties and phase behavior of intrinsically disordered proteins. Proc. Natl Acad. Sci. USA 115, 9929–9934 (2018). · DOI
  81. Michaud-Agrawal, N., Denning, E. J., Woolf, T. B. & Beckstein, O. MDAnalysis: a toolkit for the analysis of molecular dynamics simulations. J. Comput. Chem. 32, 2319–2327 (2011). · DOI
  82. Gowers, R. J. et al. MDAnalysis: a Python package for the rapid analysis of molecular dynamics simulations. In Proceedings of the 15th Python in Science Conference (eds Benthall, S. & Rostrup, S.) (SciPy, 2016). · DOI
  83. Dignon, G. L., Zheng, W., Kim, Y. C., Best, R. B. & Mittal, J. Sequence determinants of protein phase behavior from a coarse-grained model. PLoS Comput. Biol. 14, e1005941 (2018). · DOI
  84. Schrimpf, W., Barth, A., Hendrix, J. & Lamb, D. C. PAM: a framework for integrated analysis of imaging, single-molecule, and ensemble fluorescence data. Biophys. J. 114, 1518–1528 (2018). · DOI
  85. Hellenkamp, B. et al. Precision and accuracy of single-molecule FRET measurements—a multi-laboratory benchmark study. Nat. Methods 15, 669–676 (2018). · DOI · Paper page
  86. Kudryavtsev, V. et al. Combining MFD and PIE for accurate single-pair Förster resonance energy transfer measurements. ChemPhysChem 13, 1060–1078 (2012). · DOI
  87. Tomov, T. E. et al. Disentangling subpopulations in single-molecule FRET and ALEX experiments with photon distribution analysis. Biophys. J. 102, 1163–1173 (2012). · DOI

Metadata

Authors
Scholl, D., Boyd, T., Latham, A. P., Salazar, A., Khan, A. M. A. M., Boeynaems, S., Holehouse, A. S., Lander, G. C., Sali, A., Park, D., Deniz, A. A., & Lasker, K.
Affiliations
  • Scripps Research Institute (US)
  • University of California, San Francisco (US)
  • Baylor College of Medicine (US)
  • Washington University in St. Louis (US)
Corresponding author
  • Park, Donghyun
  • Deniz, Ashok A.
  • Lasker, Keren
Venue
Nature Structural & Molecular Biology
Published
2026-02-02
Volume
33
Issue
3
Pages
420-432
ISSN
  • 1545-9985 (ISSN-L)
  • 1545-9993
OpenAlex
W7127082149
Cited by
14
References
5
Citations by year
2026: 12
FWCI
24.80
Type
journal article
Language
en
Open access
Locations
  • Nature Structural & Molecular Biology (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
  • Digital Commons-TMC (Texas Medical Center) (submittedVersion)
Funders
  • National Institutes of Health (1s10od032467-01)
  • National Institute of General Medical Sciences (R01GM083960, F32GM150243, R35GM130375)
  • National Institute of Neurological Disorders and Stroke (DP2NS142714, R01NS095892)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Microtubule and mitosis dynamics
Keywords
PopZ, biomolecular condensates, helical domain, intrinsically disordered regions, phase separation, cell growth

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