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Microtubule assembly and disassembly dynamics model: Exploring dynamic instability and identifying features of Microtubules’ Growth, Catastrophe, Shortening, and Rescue
Microtubules (MTs), a cellular structure element, exhibit dynamic instability and can switch stochastically from growth to shortening; but the factors that trigger these processes at the molecular level are not understood. We developed a 3D Microtubule Assembly and Disassembly DYnamics (MADDY) model...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Research Network of Computational and Structural Biotechnology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861655/ https://www.ncbi.nlm.nih.gov/pubmed/35242287 http://dx.doi.org/10.1016/j.csbj.2022.01.028 |
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author | Kliuchnikov, Evgenii Klyshko, Eugene Kelly, Maria S. Zhmurov, Artem Dima, Ruxandra I. Marx, Kenneth A. Barsegov, Valeri |
author_facet | Kliuchnikov, Evgenii Klyshko, Eugene Kelly, Maria S. Zhmurov, Artem Dima, Ruxandra I. Marx, Kenneth A. Barsegov, Valeri |
author_sort | Kliuchnikov, Evgenii |
collection | PubMed |
description | Microtubules (MTs), a cellular structure element, exhibit dynamic instability and can switch stochastically from growth to shortening; but the factors that trigger these processes at the molecular level are not understood. We developed a 3D Microtubule Assembly and Disassembly DYnamics (MADDY) model, based upon a bead-per-monomer representation of the αβ-tubulin dimers forming an MT lattice, stabilized by the lateral and longitudinal interactions between tubulin subunits. The model was parameterized against the experimental rates of MT growth and shortening, and pushing forces on the Dam1 protein complex due to protofilaments splaying out. Using the MADDY model, we carried out GPU-accelerated Langevin simulations to access dynamic instability behavior. By applying Machine Learning techniques, we identified the MT characteristics that distinguish simultaneously all four kinetic states: growth, catastrophe, shortening, and rescue. At the cellular 25 μM tubulin concentration, the most important quantities are the MT length [Formula: see text] , average longitudinal curvature [Formula: see text] , MT tip width [Formula: see text] , total energy of longitudinal interactions in MT lattice [Formula: see text] , and the energies of longitudinal and lateral interactions required to complete MT to full cylinder [Formula: see text] and [Formula: see text]. At high 250 μM tubulin concentration, the most important characteristics are [Formula: see text] , [Formula: see text] , number of hydrolyzed αβ-tubulin dimers [Formula: see text] and number of lateral interactions per helical pitch [Formula: see text] in MT lattice, energy of lateral interactions in MT lattice [Formula: see text] , and energy of longitudinal interactions in MT tip [Formula: see text]. These results allow greater insights into what brings about kinetic state stability and the transitions between states involved in MT dynamic instability behavior. |
format | Online Article Text |
id | pubmed-8861655 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-88616552022-03-02 Microtubule assembly and disassembly dynamics model: Exploring dynamic instability and identifying features of Microtubules’ Growth, Catastrophe, Shortening, and Rescue Kliuchnikov, Evgenii Klyshko, Eugene Kelly, Maria S. Zhmurov, Artem Dima, Ruxandra I. Marx, Kenneth A. Barsegov, Valeri Comput Struct Biotechnol J Research Article Microtubules (MTs), a cellular structure element, exhibit dynamic instability and can switch stochastically from growth to shortening; but the factors that trigger these processes at the molecular level are not understood. We developed a 3D Microtubule Assembly and Disassembly DYnamics (MADDY) model, based upon a bead-per-monomer representation of the αβ-tubulin dimers forming an MT lattice, stabilized by the lateral and longitudinal interactions between tubulin subunits. The model was parameterized against the experimental rates of MT growth and shortening, and pushing forces on the Dam1 protein complex due to protofilaments splaying out. Using the MADDY model, we carried out GPU-accelerated Langevin simulations to access dynamic instability behavior. By applying Machine Learning techniques, we identified the MT characteristics that distinguish simultaneously all four kinetic states: growth, catastrophe, shortening, and rescue. At the cellular 25 μM tubulin concentration, the most important quantities are the MT length [Formula: see text] , average longitudinal curvature [Formula: see text] , MT tip width [Formula: see text] , total energy of longitudinal interactions in MT lattice [Formula: see text] , and the energies of longitudinal and lateral interactions required to complete MT to full cylinder [Formula: see text] and [Formula: see text]. At high 250 μM tubulin concentration, the most important characteristics are [Formula: see text] , [Formula: see text] , number of hydrolyzed αβ-tubulin dimers [Formula: see text] and number of lateral interactions per helical pitch [Formula: see text] in MT lattice, energy of lateral interactions in MT lattice [Formula: see text] , and energy of longitudinal interactions in MT tip [Formula: see text]. These results allow greater insights into what brings about kinetic state stability and the transitions between states involved in MT dynamic instability behavior. Research Network of Computational and Structural Biotechnology 2022-01-31 /pmc/articles/PMC8861655/ /pubmed/35242287 http://dx.doi.org/10.1016/j.csbj.2022.01.028 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Kliuchnikov, Evgenii Klyshko, Eugene Kelly, Maria S. Zhmurov, Artem Dima, Ruxandra I. Marx, Kenneth A. Barsegov, Valeri Microtubule assembly and disassembly dynamics model: Exploring dynamic instability and identifying features of Microtubules’ Growth, Catastrophe, Shortening, and Rescue |
title | Microtubule assembly and disassembly dynamics model: Exploring dynamic instability and identifying features of Microtubules’ Growth, Catastrophe, Shortening, and Rescue |
title_full | Microtubule assembly and disassembly dynamics model: Exploring dynamic instability and identifying features of Microtubules’ Growth, Catastrophe, Shortening, and Rescue |
title_fullStr | Microtubule assembly and disassembly dynamics model: Exploring dynamic instability and identifying features of Microtubules’ Growth, Catastrophe, Shortening, and Rescue |
title_full_unstemmed | Microtubule assembly and disassembly dynamics model: Exploring dynamic instability and identifying features of Microtubules’ Growth, Catastrophe, Shortening, and Rescue |
title_short | Microtubule assembly and disassembly dynamics model: Exploring dynamic instability and identifying features of Microtubules’ Growth, Catastrophe, Shortening, and Rescue |
title_sort | microtubule assembly and disassembly dynamics model: exploring dynamic instability and identifying features of microtubules’ growth, catastrophe, shortening, and rescue |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861655/ https://www.ncbi.nlm.nih.gov/pubmed/35242287 http://dx.doi.org/10.1016/j.csbj.2022.01.028 |
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