Kliuchnikov Evgenii, Klyshko Eugene, Kelly Maria S, Zhmurov Artem, Dima Ruxandra I, Marx Kenneth A, Barsegov Valeri
Department of Chemistry, University of Massachusetts, Lowell, MA 01854, USA.
Department of Chemistry, University of Cincinnati, Cincinnati, OH 45221, USA.
Comput Struct Biotechnol J. 2022 Jan 31;20:953-974. doi: 10.1016/j.csbj.2022.01.028. eCollection 2022.
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--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 , average longitudinal curvature , MT tip width , total energy of longitudinal interactions in MT lattice , and the energies of longitudinal and lateral interactions required to complete MT to full cylinder and . At high 250 μM tubulin concentration, the most important characteristics are , , number of hydrolyzed αβ-tubulin dimers and number of lateral interactions per helical pitch in MT lattice, energy of lateral interactions in MT lattice , and energy of longitudinal interactions in MT tip . These results allow greater insights into what brings about kinetic state stability and the transitions between states involved in MT dynamic instability behavior.
微管（MTs）是一种细胞结构元件，具有动态不稳定性，能够随机地从生长状态转变为缩短状态；但在分子水平上触发这些过程的因素尚不清楚。我们基于形成微管晶格的αβ - 微管蛋白二聚体的珠子 - 单体表示法，开发了一种三维微管组装与拆卸动力学（MADDY）模型，该模型通过微管蛋白亚基之间的横向和纵向相互作用得以稳定。该模型根据微管生长和缩短的实验速率以及原纤维展开对Dam1蛋白复合物产生的推力进行参数化。使用MADDY模型，我们进行了GPU加速的朗之万模拟以研究动态不稳定性行为。通过应用机器学习技术，我们确定了能够同时区分所有四种动力学状态（生长、灾变、缩短和救援）的微管特征。在细胞微管蛋白浓度为25 μM时，最重要的量是微管长度、平均纵向曲率、微管尖端宽度、微管晶格中纵向相互作用的总能量以及将微管完全组装成圆柱体所需的纵向和横向相互作用的能量。在微管蛋白浓度为250 μM的高浓度下，最重要的特征是、、水解的αβ - 微管蛋白二聚体数量以及微管晶格中每个螺旋节距的横向相互作用数量、微管晶格中横向相互作用的能量以及微管尖端纵向相互作用的能量。这些结果使我们能够更深入地了解导致动力学状态稳定性以及微管动态不稳定性行为中状态之间转变的因素。
