Rare-event simulation and analysis for elucidating mechanisms of development and disease

用于阐明发育和疾病机制的罕见事件模拟和分析

基本信息

  • 批准号:
    10611995
  • 负责人:
  • 金额:
    $ 32.96万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-05-01 至 2025-04-30
  • 项目状态:
    未结题

项目摘要

PROJECT SUMMARY. Molecular simulations complement experiments by revealing the microscopic dynamics underlying biological mechanisms and the forces promoting those dynamics. However, most biological processes involve time scales much longer than the time step of numerical integration. While there are many methods for bridging this separation of time scales to obtain equilibrium averages, further advances are needed to robustly estimate dynamical statistics. The proposed research seeks to develop general methods that can meet this need and to apply them to elucidating self-assembly mechanisms at both molecular and cellular length scales. Improving insulin therapies through rare-event analyses of short simulations. There is a pandemic in diabetes mellitus, with tremendous cost worldwide. The main treatment is insulin therapy, but it has a narrow therapeutic index, and its requirement for refrigerated transport and storage is prohibitively costly for much of the world. Insulin analogs have been engineered to have specific pharmacokinetics based on knowledge of insulin self- association, but an understanding of how insulin binds to the insulin receptor (IR) remains lacking. We seek to develop computational methods that can enable simulation and analysis of coupled folding and binding reactions and to combine these methods with recently obtained structures of IR bound to insulin and single-chain insulin (SCI) analogs to elucidate the microscopic origins of observed therapeutic activities. The study can thus ultimately lead to improved insulin therapies. We will also investigate the improved thermal properties of SCI analogs, in particular, their reduced tendency to form amyloid fibrils. The study thus also promises to yield insights into amyloid formation, with broad implications beyond insulin to neurodegenerative disorders like Parkinson's and Alzheimer's diseases. Modeling cytoskeletal processes leading to developmental patterning. Cytoskeletal dynamics underlie diverse processes, including developmental patterning, neuronal synapse formation, immunological recognition, wound healing, and tumor growth. These dynamics can be very hard to intuit because they involve balances of me- chanical forces, mechanochemistry, network assembly and dissasembly, and feedback to and from cell signaling molecules. Models thus play an important role in parsing contributing molecular processes and testing quanti- tative hypotheses. We will adapt a recently parameterized cytoskeletal model that is quantitatively predictive in vitro to elucidate mechanisms of developmental patterning in vivo. Namely, we will investigate how interactions between the small GTPase RhoA and actin assembly/dissasembly control pulsatile contractility, a widespread phenomenon that drives cortical flow, cell shape change, and tissue deformation. Then we will compare models for the localization of the evolutionarily-conserved RNA-binding protein Staufen during anterior-posterior speci- fication. In addition to aiding in understanding these key developmental processes, the simulations will yield a model that can be used to study cytoskeletal dynamics in a broad range of contexts with minimal modification.
项目总结。分子模拟通过揭示微观动力学来补充实验 潜在的生物机制和推动这些动态的力量。然而,大多数生物过程 涉及的时间尺度比数值积分的时间步长要长得多。虽然有很多方法可以用来 弥合这种时间尺度的分离以获得均衡平均值,需要进一步的进展才能强劲地 估计动态统计数据。拟议的研究旨在开发能够满足这一需求的一般方法 并将其应用于阐明分子和细胞长度尺度上的自组装机制。 通过短期模拟的罕见事件分析改进胰岛素疗法。糖尿病是一种流行病 糖尿病,在全球范围内耗资巨大。主要的治疗方法是胰岛素治疗,但它的治疗范围很窄 它的冷藏运输和储存所需费用对世界大部分地区来说都高得令人望而却步。 胰岛素类似物已被设计成具有特定的fic药代动力学,其基础是胰岛素自身的知识。 但对胰岛素如何与胰岛素受体(IR)结合仍缺乏了解。我们寻求 开发能够模拟和分析耦合折叠和结合反应的计算方法 并将这些方法与最近获得的胰岛素和单链胰岛素结合的IR结构相结合 (SCI)类似物,以阐明观察到的治疗活动的微观来源。因此,该研究最终可以 导致胰岛素疗法的改进。我们还将研究SCI类似物的改进的热性能,在 特别是,它们形成淀粉样蛋白fiBrins的倾向降低了。因此,这项研究也有望获得对 淀粉样蛋白的形成,从胰岛素到帕金森氏症和神经退行性疾病具有广泛的意义 阿尔茨海默病。 对导致发育模式的细胞骨架过程进行建模。细胞骨架动力学是不同的基础 过程,包括发育模式,神经元突触形成,免疫识别,创伤 治愈和肿瘤生长。这些动态可能很难直觉,因为它们涉及到我的平衡- 机械力、机械力化学、网络组装和分裂,以及细胞信号的反馈 分子。因此,模型在解析起作用的分子过程和测试量子过程中起着重要作用。 推测性假设。我们将采用一个最近参数化的细胞骨架模型,该模型可以定量预测 体外实验,以阐明体内发育模式的机制。也就是说,我们将调查交互是如何 在小GTP酶RhoA和肌动蛋白组装/分散之间可控制搏动性收缩,广泛存在 导致皮质fl下降、细胞形状改变和组织变形的现象。然后我们将比较模型 对于进化保守的RNA结合蛋白Staufen在前后部物种中的定位 fi阳离子。除了有助于理解这些关键的发育过程外,模拟还将产生一个 该模型可用于在具有最小Modifi阳离子的大范围上下文中研究细胞骨架动力学。

项目成果

期刊论文数量(11)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Computing transition path theory quantities with trajectory stratification
  • DOI:
    10.1063/5.0087058
  • 发表时间:
    2022-07-21
  • 期刊:
  • 影响因子:
    4.4
  • 作者:
    Vani, Bodhi P.;Weare, Jonathan;Dinner, Aaron R.
  • 通讯作者:
    Dinner, Aaron R.
Predicting rare events using neural networks and short-trajectory data
  • DOI:
    10.1016/j.jcp.2023.112152
  • 发表时间:
    2022-08
  • 期刊:
  • 影响因子:
    4.1
  • 作者:
    J. Strahan;J. Finkel;A. Dinner;J. Weare
  • 通讯作者:
    J. Strahan;J. Finkel;A. Dinner;J. Weare
Long-Time-Scale Predictions from Short-Trajectory Data: A Benchmark Analysis of the Trp-Cage Miniprotein.
  • DOI:
    10.1021/acs.jctc.0c00933
  • 发表时间:
    2021-05-11
  • 期刊:
  • 影响因子:
    5.5
  • 作者:
    Strahan J;Antoszewski A;Lorpaiboon C;Vani BP;Weare J;Dinner AR
  • 通讯作者:
    Dinner AR
Understanding the sources of error in MBAR through asymptotic analysis
通过渐近分析了解 MBAR 的误差来源
  • DOI:
    10.1063/5.0147243
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Li, Xiang Sherry;Van Koten, Brian;Dinner, Aaron R.;Thiede, Erik H.
  • 通讯作者:
    Thiede, Erik H.
Augmented transition path theory for sequences of events
事件序列的增强转移路径理论
  • DOI:
    10.1063/5.0098587
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Lorpaiboon, Chatipat;Weare, Jonathan;Dinner, Aaron R.
  • 通讯作者:
    Dinner, Aaron R.
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Aaron Dinner其他文献

Aaron Dinner的其他文献

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{{ truncateString('Aaron Dinner', 18)}}的其他基金

Rare-event simulation and analysis for elucidating mechanisms of development and disease
用于阐明发育和疾病机制的罕见事件模拟和分析
  • 批准号:
    10396476
  • 财政年份:
    2020
  • 资助金额:
    $ 32.96万
  • 项目类别:
Robust rare event simulation for protein folding and disease-related aggregation
蛋白质折叠和疾病相关聚集的稳健罕见事件模拟
  • 批准号:
    9316663
  • 财政年份:
    2013
  • 资助金额:
    $ 32.96万
  • 项目类别:
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