Cited by 1Microtubule and mitosis dynamicsProtein Structure and DynamicsCellular Mechanics and Interactions

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On the curvature and relaxation of microtubule plus-end tips

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Abstract

ABSTRACT Microtubules are essential cytoskeletal components with a broad range of functions in which the structure and dynamics of their plus-end tips play critical roles. Existing mechanistic models explain the tips curving dynamics in different ways: the allosteric model suggests that GTP hydrolysis induces conformational changes in tubulin subunits that destabilize the lattice, leading to protofilament curving and depolymerization, while the lattice model posits that GTP hydrolysis directly destabilizes the microtubule lattice. However, the effect of GTP hydrolysis on the curving dynamics of microtubule tips remains incompletely understood. In this study, we employed a multiscale modeling approach, combining all-atom molecular dynamics simulations with Brownian dynamics simulations, to investigate the relaxation of microtubule plus-end tips into curved configurations. Our results show that both GDP- and GTP-bound tips exhibit an outward bending of protofilaments into curved, ram’s horn-like structures, characterized by a linear relationship between curvature and distance from the plus-end tip. These observations align with experimental cryo-ET images of microtubule plus-end tips in different nucleotide states. Collectively, our findings suggest that the outward bending of protofilaments at the plus-end tip is an intrinsic feature of microtubules, independent of the nucleotide state. SIGNIFICANCE Understanding how microtubules change shape is crucial for elucidating key cellular processes such as cell division and shape maintenance, which are fundamental to both physiological function and disease progression. This study supports the concept that the microtubule plus-end tip relaxation does not align with models that couple shape changes to GTP hydrolysis, at least for the topmost tubulin heterodimers. By interfacing bottom-up multiscale modeling — using the longest reported atomistic molecular dynamics simulations of microtubule tips— with existing cryo-ET data, it is shown that protofilament bending operates independently of nucleotide hydrolysis and likely depolymerization. These findings highlight the need for a new conceptual framework that separates GTP hydrolysis from microtubule tip flaring.

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Research works printed but not linked: 92

  1. Hawkins, 2010, Mechanics of microtubules, J. Biomech., 43, 23, 10.1016/j.jbiomech.2009.09.005 · DOI
  2. Mitchison, 1988, Microtubule dynamics and kinetochore function in mitosis, Annu. Rev. Cell Biol., 4, 527, 10.1146/annurev.cb.04.110188.002523 · DOI
  3. Gilpin, 2020, The multiscale physics of cilia and flagella, Nat. Rev. Phys., 2, 74, 10.1038/s42254-019-0129-0 · DOI
  4. Henty-Ridilla, 2016, Accelerated actin filament polymerization from microtubule plus ends, Science (Wash. D C), 352, 1004, 10.1126/science.aaf1709 · DOI
  5. Kaverina, 1998, Targeting, capture, and stabilization of microtubules at early focal adhesions, J. Cell Biol., 142, 181, 10.1083/jcb.142.1.181 · DOI
  6. Witte, 2008, Microtubule stabilization specifies initial neuronal polarization, J. Cell Biol., 180, 619, 10.1083/jcb.200707042 · DOI · Paper page
  7. Stehbens, 2014, CLASPs link focal-adhesion-associated microtubule capture to localized exocytosis and adhesion site turnover, Nat. Cell Biol., 16, 561, 10.1038/ncb2975 · DOI
  8. Tabdanov, 2021, Engineering T cells to enhance 3D migration through structurally and mechanically complex tumor microenvironments, Nat. Commun., 12, 2815, 10.1038/s41467-021-22985-5 · DOI
  9. Mitchison, 1984, Dynamic instability of microtubule growth, Nature, 312, 237, 10.1038/312237a0 · DOI · Paper page
  10. Gouveia, 2010, Cell and molecular biology of microtubule plus end tracking proteins: end binding proteins and their partners, Int. Rev. Cell Mol. Biol., 285, 1
  11. Dogterom, 2019, Actin–microtubule crosstalk in cell biology, Nat. Rev. Mol. Cell Biol., 20, 38, 10.1038/s41580-018-0067-1 · DOI
  12. Weisenberg, 1976, Tubulin-nucleotide interactions during the polymerization and depolymerization of microtubules, Biochemistry, 15, 4248, 10.1021/bi00664a018 · DOI
  13. Erickson, 1992, Microtubule dynamic instability and GTP hydrolysis, Annu. Rev. Biophys. Biomol. Struct., 21, 145, 10.1146/annurev.bb.21.060192.001045 · DOI
  14. Drechsel, 1994, The minimum GTP cap required to stabilize microtubules, Curr. Biol., 4, 1053, 10.1016/S0960-9822(00)00243-8 · DOI
  15. Caplow, 1996, Evidence that a single monolayer tubulin-GTP cap is both necessary and sufficient to stabilize microtubules, Mol. Biol. Cell, 7, 663, 10.1091/mbc.7.4.663 · DOI
  16. Seetapun, 2012, Estimating the microtubule GTP cap size in vivo, Curr. Biol., 22, 1681, 10.1016/j.cub.2012.06.068 · DOI
  17. McIntosh, 2018, Microtubules grow by the addition of bent guanosine triphosphate tubulin to the tips of curved protofilaments, J. Cell Biol., 217, 2691, 10.1083/jcb.201802138 · DOI
  18. Beckett, 2023, Unveiling the catalytic mechanism of GTP hydrolysis in microtubules, Proc. Natl. Acad. Sci. USA, 120, 10.1073/pnas.2305899120 · DOI
  19. Mandelkow, 1991, Microtubule dynamics and microtubule caps: a time-resolved cryo-electron microscopy study, J. Cell Biol., 114, 977, 10.1083/jcb.114.5.977 · DOI
  20. Müller-Reichert, 1998, Structural changes at microtubule ends accompanying GTP hydrolysis: information from a slowly hydrolyzable analogue of GTP, guanylyl (α, β) methylenediphosphonate, Proc. Natl. Acad. Sci. USA, 95, 3661, 10.1073/pnas.95.7.3661 · DOI
  21. Melki, 1989, Cold depolymerization of microtubules to double rings: geometric stabilization of assemblies, Biochemistry, 28, 9143, 10.1021/bi00449a028 · DOI
  22. Nogales, 2003, Tubulin rings: which way do they curve?, Curr. Opin. Struct. Biol., 13, 256, 10.1016/S0959-440X(03)00029-0 · DOI
  23. Rice, 2008, The lattice as allosteric effector: structural studies of αβ-and γ-tubulin clarify the role of GTP in microtubule assembly, Proc. Natl. Acad. Sci. USA, 105, 5378, 10.1073/pnas.0801155105 · DOI
  24. Manka, 2018, The role of tubulin–tubulin lattice contacts in the mechanism of microtubule dynamic instability, Nat. Struct. Mol. Biol., 25, 607, 10.1038/s41594-018-0087-8 · DOI · Paper page
  25. VandenBeldt, 2006, Kinetochores use a novel mechanism for coordinating the dynamics of individual microtubules, Curr. Biol., 16, 1217, 10.1016/j.cub.2006.04.046 · DOI
  26. Zovko, 2008, Microtubule plus-end conformations and dynamics in the periphery of interphase mouse fibroblasts, Mol. Biol. Cell, 19, 3138, 10.1091/mbc.e07-07-0681 · DOI
  27. Höög, 2011, Electron tomography reveals a flared morphology on growing microtubule ends, J. Cell Sci., 124, 693, 10.1242/jcs.072967 · DOI
  28. McIntosh, 2013, Conserved and divergent features of kinetochores and spindle microtubule ends from five species, J. Cell Biol., 200, 459, 10.1083/jcb.201209154 · DOI
  29. Gudimchuk, 2020, Mechanisms of microtubule dynamics and force generation examined with computational modeling and electron cryotomography, Nat. Commun., 11, 3765, 10.1038/s41467-020-17553-2 · DOI
  30. Gudimchuk, 2021, Regulation of microtubule dynamics, mechanics and function through the growing tip, Nat. Rev. Mol. Cell Biol., 22, 777, 10.1038/s41580-021-00399-x · DOI
  31. van den Berg, 2023, CSPP1 stabilizes growing microtubule ends and damaged lattices from the luminal side, J. Cell Biol., 222, 10.1083/jcb.202208062 · DOI
  32. Kalutskii, 2025, Microtubule dynamics are defined by conformations and stability of clustered protofilaments, Proc. Natl. Acad. Sci. USA, 122, 10.1073/pnas.2424263122 · DOI
  33. Buey, 2006, The nucleotide switch of tubulin and microtubule assembly: a polymerization-driven structural change, Biochemistry, 45, 5933, 10.1021/bi060334m · DOI
  34. Grafmüller, 2011, Intrinsic bending of microtubule protofilaments, Structure, 19, 409, 10.1016/j.str.2010.12.020 · DOI
  35. Nawrotek, 2011, The determinants that govern microtubule assembly from the atomic structure of GTP-tubulin, J. Mol. Biol., 412, 35, 10.1016/j.jmb.2011.07.029 · DOI
  36. Zhou, 2023, Structural insights into the mechanism of GTP initiation of microtubule assembly, Nat. Commun., 14, 5980, 10.1038/s41467-023-41615-w · DOI
  37. Wagstaff, 2017, A polymerization-associated structural switch in FtsZ that enables treadmilling of model filaments, mBio, 8, 10.1128/mBio.00254-17 · DOI
  38. Wagstaff, 2023, Diverse cytomotive actins and tubulins share a polymerization switch mechanism conferring robust dynamics, Sci. Adv., 9, 10.1126/sciadv.adf3021 · DOI
  39. Wagstaff, 2018, Prokaryotic cytoskeletons: protein filaments organizing small cells, Nat. Rev. Microbiol., 16, 187, 10.1038/nrmicro.2017.153 · DOI
  40. Igaev, 2022, Bending-torsional elasticity and energetics of the plus-end microtubule tip, Proc. Natl. Acad. Sci., 119, 10.1073/pnas.2115516119 · DOI
  41. Ogunmolu, 2021, Microtubule plus-end regulation by centriolar cap proteins, bioRxiv
  42. Chrétien, 1995, Structure of growing microtubule ends: two-dimensional sheets close into tubes at variable rates, J. Cell Biol., 129, 1311, 10.1083/jcb.129.5.1311 · DOI
  43. Guesdon, 2016, EB1 interacts with outwardly curved and straight regions of the microtubule lattice, Nat. Cell Biol., 18, 1102, 10.1038/ncb3412 · DOI
  44. Stewman, 2020, Dynamic instability from non-equilibrium structural transitions on the energy landscape of microtubule, Cell Syst., 11, 608, 10.1016/j.cels.2020.09.008 · DOI
  45. Zhang, 2018, Separating the effects of nucleotide and EB binding on microtubule structure, Proc. Natl. Acad. Sci. USA, 115, E6191
  46. Eswar, 2006, Comparative Protein Structure Modeling Using Modeller, Curr. Protoc. Bioinformatics, 15, 5.6.30, 10.1002/0471250953.bi0506s15 · DOI
  47. Tong, 2020, Microtubule simulations provide insight into the molecular mechanism underlying dynamic instability, Biophys. J., 118, 2938, 10.1016/j.bpj.2020.04.028 · DOI
  48. Humphrey, 1996, VMD: visual molecular dynamics, J. Mol. Graph., 14, 33, 10.1016/0263-7855(96)00018-5 · DOI · Paper page
  49. Van Der Spoel, 2005, GROMACS: fast, flexible, and free, J. Comput. Chem., 26, 1701, 10.1002/jcc.20291 · DOI
  50. Huang, 2017, CHARMM36m: an improved force field for folded and intrinsically disordered proteins, Nat. Methods, 14, 71, 10.1038/nmeth.4067 · DOI
  51. Darden, 1993, Particle mesh Ewald: An N(N) method for Ewald sums in large systems, J. Chem. Phys., 98, 10089, 10.1063/1.464397 · DOI
  52. Parrinello, 1981, Polymorphic transitions in single crystals: A new molecular dynamics method, J. Appl. Phys., 52, 7182, 10.1063/1.328693 · DOI
  53. Bussi, 2007, Canonical sampling through velocity rescaling, J. Chem. Phys., 126, 10.1063/1.2408420 · DOI
  54. Saunders, 2013, Coarse-graining methods for computational biology, Annu. Rev. Biophys., 42, 73, 10.1146/annurev-biophys-083012-130348 · DOI
  55. Wu, 2023, K-Means Clustering Coarse-Graining (KMC-CG): A Next Generation Methodology for Determining Optimal Coarse-Grained Mappings of Large Biomolecules, J. Chem. Theor. Comput., 19, 8987, 10.1021/acs.jctc.3c01053 · DOI
  56. Peng, 2023, OpenMSCG: A software tool for bottom-up coarse-graining, J. Phys. Chem. B, 127, 8537, 10.1021/acs.jpcb.3c04473 · DOI
  57. Ermak, 1978, Brownian dynamics with hydrodynamic interactions, J. Chem. Phys., 69, 1352, 10.1063/1.436761 · DOI
  58. Długosz, 2011, Diffusion in crowded biological environments: applications of Brownian dynamics, BMC Biophys., 4, 1, 10.1186/2046-1682-4-3 · DOI
  59. Huber, 2019, Brownian dynamics simulations of biological molecules, Trends Chem., 1, 727, 10.1016/j.trechm.2019.07.008 · DOI
  60. Szymczak, 2011, Hydrodynamic effects in proteins, J. Phys. Condens. Matter, 23, 10.1088/0953-8984/23/3/033102 · DOI
  61. Thompson, 2022, LAMMPS-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales, Comput. Phys. Commun., 271, 10.1016/j.cpc.2021.108171 · DOI · Paper page
  62. Skolnick, 2016, Perspective: On the importance of hydrodynamic interactions in the subcellular dynamics of macromolecules, J. Chem. Phys., 145, 10.1063/1.4962258 · DOI
  63. Zuk, 2018, GRPY: an accurate bead method for calculation of hydrodynamic properties of rigid biomacromolecules, Biophys. J., 115, 782, 10.1016/j.bpj.2018.07.015 · DOI
  64. Szymczak, 2007, Proteins in a shear flow, J. Chem. Phys., 127, 155106, 10.1063/1.2795725 · DOI
  65. Waszkiewicz, 2023, Pychastic: Precise Brownian dynamics using Taylor-Itō integrators in Python, SciPost Physics Codebases, 11
  66. Zuk, 2014, Rotne–Prager–Yamakawa approximation for different-sized particles in application to macromolecular bead models, J. Fluid Mech., 741, 10.1017/jfm.2013.668 · DOI
  67. Varadi, 2022, AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models, Nucleic Acids Res., 50, D439, 10.1093/nar/gkab1061 · DOI · Paper page
  68. Ansari, 1992, The role of solvent viscosity in the dynamics of protein conformational changes, Science, 256, 1796, 10.1126/science.1615323 · DOI
  69. Hagen, 2010, Solvent viscosity and friction in protein folding dynamics, Curr. Protein Pept. Sci., 11, 385, 10.2174/138920310791330596 · DOI
  70. Soper, 1996, Empirical potential Monte Carlo simulation of fluid structure, Chem. Phys., 202, 295, 10.1016/0301-0104(95)00357-6 · DOI
  71. Noid, 2013, Perspective: Coarse-grained models for biomolecular systems, J. Chem. Phys., 139, 10.1063/1.4818908 · DOI
  72. Hanke, 2018, Well-posedness of the iterative Boltzmann inversion, J. Stat. Phys., 170, 536, 10.1007/s10955-017-1944-2 · DOI
  73. Theobald, 2005, Rapid calculation of RMSDs using a quaternion-based characteristic polynomial, Acta Crystallogr. A, 61, 478, 10.1107/S0108767305015266 · DOI
  74. Michaud-Agrawal, 2011, MDAnalysis: A toolkit for the analysis of molecular dynamics simulations, J. Comput. Chem., 32, 2319, 10.1002/jcc.21787 · DOI
  75. Kremer, 1996, Computer visualization of three-dimensional image data using IMOD, J. Struct. Biol., 116, 71, 10.1006/jsbi.1996.0013 · DOI
  76. Grossfield, 2009, Quantifying uncertainty and sampling quality in biomolecular simulations, Annu. Rep. Comput. Chem., 5, 23, 10.1016/S1574-1400(09)00502-7 · DOI
  77. O’Shaughnessy, 2015, Performing t-tests to compare autocorrelated time series data collected from direct-reading instruments, J. Occup. Environ. Hyg., 12, 743, 10.1080/15459624.2015.1044603 · DOI
  78. Welch, 1947, The generalization of ‘Student’s’problem when several different population varlances are involved, Biometrika, 34, 28
  79. Newey, 1987, A simple, positive semi-definite, heteroskedasticity and autocorrelationconsistent covariance matrix, Econometrica, 55, 703, 10.2307/1913610 · DOI · Paper page
  80. Katsuki, 2014, Ectopic A-lattice seams destabilize microtubules, Nat. Commun., 5, 3094, 10.1038/ncomms4094 · DOI
  81. Bennett, 2009, Structural mass spectrometry of the αβ-tubulin dimer supports a revised model of microtubule assembly, Biochemistry, 48, 4858, 10.1021/bi900200q · DOI
  82. Ji, 2011, Coarse-grained mechanochemical model for simulating the dynamic behavior of microtubules, Phys. Rev. E, 84, 10.1103/PhysRevE.84.031933 · DOI
  83. Ji, 2011, Mechanochemical modeling of dynamic microtubule growth involving sheet-to-tube transition, PLoS One, 6, 10.1371/journal.pone.0029049 · DOI
  84. Jain, 2015, Statistical Mechanics Provides Novel Insights into Microtubule Stability and Mechanism of Shrinkage, PLoS Comput. Biol., 11, 10.1371/journal.pcbi.1004099 · DOI
  85. Nogales, 1999, High-resolution model of the microtubule, Cell, 96, 79, 10.1016/S0092-8674(00)80961-7 · DOI
  86. Roostalu, 2020, The speed of GTP hydrolysis determines GTP cap size and controls microtubule stability, eLife, 9, 10.7554/eLife.51992 · DOI
  87. Simon, 1990, The structure of microtubule ends during the elongation and shortening phases of dynamic instability examined by negative-stain electron microscopy, J. Cell Sci., 96, 571, 10.1242/jcs.96.4.571 · DOI
  88. Zakharov, 2015, Molecular and mechanical causes of microtubule catastrophe and aging, Biophys. J., 109, 2574, 10.1016/j.bpj.2015.10.048 · DOI
  89. Ulyanov, 2021, Brownian dynamics simulation of protofilament relaxation during rapid freezing, PLoS One, 16
  90. González, 2010, The shear viscosity of rigid water models, J. Chem. Phys., 132, 10.1063/1.3330544 · DOI
  91. Castle, 2013, Brownian dynamics of subunit addition-loss kinetics and thermodynamics in linear polymer self-assembly, Biophys. J., 105, 2528, 10.1016/j.bpj.2013.10.009 · DOI
  92. Wieczorek, 2013, Macromolecular crowding pushes catalyzed microtubule growth to near the theoretical limit, Cell. Mol. Bioeng., 6, 383, 10.1007/s12195-013-0292-9 · DOI

Metadata

Authors
Skóra, T., Wu, J., Beckett, D., Xue, W., Voth, G. A., & Bidone, T. C.
Affiliations
  • University of Utah (US)
  • University of Chicago (US)
Corresponding author
Bidone, Tamara C.
Venue
Biophysical Journal
Publisher
Elsevier BV
Published
2026-01
Volume
125
Issue
2
Pages
406-419
ISSN
0006-3495 (ISSN-L)
OpenAlex
W4412031355
Cited by
1
References
4
Citations by year
2026: 1
FWCI
0.50
Type
journal article
Language
en
Open access
Locations
  • Biophysical Journal (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed Central (submittedVersion, cc-by-nc)
Funders
  • Office of Science
  • Research Computing Center, University of Chicago
  • Basic Energy Sciences (DE-SC0023318)
  • National Institutes of Health
  • University of Utah
  • U.S. Department of Energy
  • NIH National Institute of General Medical Sciences (F32 GM140646)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Microtubule and mitosis dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Cellular Mechanics and Interactions
Keywords
GTP hydrolysis, microtubule lattice, allosteric model, lattice model, cytoskeletal dynamics

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