Computational Investigations of the Mechanisms Behind Microtubule Catastrophe

项目总结 微管(Mt)是真核细胞骨架的最大组成部分，促进了大量的 多种功能，包括细胞内运输、细胞运动和细胞分裂。在有丝分裂期间，MTS 聚集形成有丝分裂纺锤体，使其成为许多成功化疗的有效药物靶点 毒剂，包括紫杉醇和长春花碱，称为纺锤形毒药。MT靶向药物通过干扰作用发挥作用 动态不稳定性(DI)：MTS从聚合快速切换到解聚的能力(参见 作为灾难)，反之亦然。紫杉醇通过降低灾害率而长春花碱鼓励 灾难和抑制聚合。对MT灾变的充分认识将极大地帮助设计 纺锤形毒药与较少的偏离目标的影响，以及极大地促进了对DI的一般理解。 每个MT由αβ-微管蛋白异源二聚体组成，在排列的原丝(PF)中头到尾堆积 横向形成一个中空的管子。α-微管蛋白和β-微管蛋白均结合鸟苷三磷酸及其水解酶 β-微管蛋白结合部位的Gdp(鸟苷二磷酸)被假设为在MT晶格上诱导应力。 这种压力逐渐积累，直到MT末端的亚基经历GTP水解，在这一点上PFS开始 剥离，灾难就发生了。GTP水解和聚合之间的滞后形成了一个结构 称为GTP帽：MT端的一组尚未水解GTP的亚基，释放 生成无机磷酸盐(PI)，或经历结构转变。最近的研究引起了人们对 关于这种转变的性质的领域和对基本机制的原子论理解将导致 对灾难的充分理解。我建议通过计算解决灾难的三个关键方面： GTP的水解、PI的释放以及PFS之间的结构耦合导致突变的机理。 首先，我将使用改进的采样方法来揭示GTP水解酶的机制， 重点是属于α-微管蛋白的潜在催化残基，它位于β-微管蛋白之上 聚合形成活性中心。随后，我将开发新的计算技术来确定 PI的途径在水解后释放，并检测释放后结构变化的可能性。最后，我 将使用从先前研究中确定的速率来开发完整MT的粗粒度(CG)模型，能够 经历灾难来检查相邻亚基中的水解和PI释放如何影响潜在的 这些反应发生在特定的亚基中。这将给出一个史无前例的详细视角 GTP上限和导致灾难的步骤。此外，我将与两位领先的实验者合作 在MT社区中开发专门测试我的假设的突变体，并获得 掺杂了纺锤体毒药的MTS。这将使我能够将药物的影响整合到CG模型中，并 研究它们的影响如何沿MT传播。这些结果和所开发的模型将极大地推动 对DI的了解，有望在未来开发更温和的MT靶向治疗方法。

PROJECT SUMMARY Microtubules (MTs) constitute the largest components of the eukaryotic cytoskeleton and facilitate a plethora of diverse functions including intracellular transport, cellular motility, and, cell division. During mitosis, MTs aggregate to form the mitotic spindle, making them a potent drug target for many successful chemotherapeutic agents, including paclitaxel and vinblastine, known as spindle poisons. MT-targeting drugs operate by interfering with dynamic instability (DI): the ability of MTs to rapidly switch from polymerizing to depolymerizing (referred to as catastrophe) and vice-versa. Paclitaxel operates by decreasing catastrophe rate while vinblastine encourages catastrophe and inhibits polymerization. A full understanding of MT catastrophe will greatly aid in the design of spindle poisons with fewer off-target effects, as well as greatly advance general understanding of DI. Each MT is composed of αβ-tubulin heterodimers, stacked head-to-tail in protofilaments (PFs) which are aligned laterally to form a hollow tube. Both α- and β-tubulin bind guanosine triphosphate (GTP) and hydrolysis of GTP to GDP (guanosine diphosphate) at the β-tubulin binding site is hypothesized to induce stress on the MT lattice. This stress gradually builds until the subunits at the MT end undergo GTP hydrolysis, at which point PFs begin to peel apart and catastrophe has occurred. Lag between GTP hydrolysis and polymerization creates a construct referred to as the GTP cap: a group of subunits at the MT end that have yet to hydrolyze GTP, release the product inorganic phosphate (Pi), or undergo a structural transition. Recent studies have caused doubt in the field on the nature of this transition and an atomistic understanding of the underlying mechanisms will lead to a full understanding of catastrophe. I propose to computationally resolve three key aspects of catastrophe: the mechanism of GTP hydrolysis, the release of Pi, and the structural coupling between PFs leading to catastrophe. First, I will use enhanced sampling methodology to uncover the enzymatic mechanism of GTP hydrolysis, with emphasis placed on potential catalytic residues belonging to α-tubulin, which sits atop β-tubulin upon polymerization to form the active site. Subsequently, I will develop novel computational techniques to determine the pathway of Pi release post-hydrolysis and examine the potential for structural change upon release. Lastly, I will develop a coarse-grained (CG) model of a full MT, using rates determined from the previous studies, able to undergo catastrophe to examine how hydrolysis and Pi release in neighboring subunits affects the potential for these reactions to occur in a particular subunit. This will give an unprecedentedly detailed view of the loss of the GTP cap and the steps leading to catastrophe. Additionally, I will collaborate with two leading experimentalists in the MT community to develop mutants that specifically test my hypotheses and to obtain lattice parameters of MTs doped with spindle poisons. This will allow me to integrate the effects of drugs into the CG model and examine how their effects propagate along an MT. These results and the developed models will greatly advance the understanding of DI and hopefully lead to the development of gentler MT-targeting therapies in the future.