A mechano-geometric framework to characterize macromolecular ensembles

表征大分子整体的力学几何框架

• 批准号: 401512690
• 负责人: Professorin Dr.-Ing. Sigrid Leyendecker
• 金额: --
• 依托单位: Lehrstuhl für Technische Dynamik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/401512690?language=en
• 关键词: mechano; geometric; framework; characterize; macromolecular

Macromolecules like protein, RNA, and DNA perform their cellular function through dynamically changing their three-dimensional structure. Understanding the overall structural ensemble of such a molecule is crucial to reveal its key roles, and potentially intervene to restore lost function. While traditional Molecular Dynamics can provide atomically detailed trajectories, their computational cost is still tremendous, often limiting analysis to small spatial or temporal scales. Hence, lower accuracy, but high throughput methods are valuable to obtain first insights and guide more detailed, subsequent analysis focused on the area of interest. Robotics-inspired kinematic methods have been applied with great success to coarse-grained molecular modeling: their efficient nature allows for fast, yet reliable insights into molecular motion, and provide useful tools for data interpretation and integration. In this proposal, we aim to extend our existing kinematic molecular modeling software into a unified, mechano-geometric framework to study conformational ensembles of complex macromolecules. We will combine geometric constraint formulations of atomic interactions with rigidity theory to gain first-order insights into molecular flexibility. This formulation imposes an intrinsic hierarchy of motions onto the molecule, providing an efficient tool to broadly and uniformly sample conformation space and energy landscape in such an ultra-high dimensional environment. Large changes of dihedral degrees of freedom, violation of imposed constraints, or non-native interactions via steric or hydrophobic contact can report on allosteric hotspots important for drug targeting. Initial exciting results from studying the transitions of several proteins between their two major configurations has revealed interaction networks of amino acids that were in great agreement with published experimental data. Directly integrating experimental data will help validate and improve our computational algorithm, and allow for data-driven conformational exploration. As experimental data is often sparse and prone to errors, our mechano-geometric modeling framework will help predict inaccessible or unknown data and design subsequent, more focused experiments.We will concentrate on experimental data from traditional and multi-temperature crystallography, new-generation X-ray free electron laser experiments and double electron-electron resonance (DEER). We already have exciting data on several systems (Isocyanide Hydratase (ICH), an enzyme of the DJ-1 hyperfamily, and two members of the G-protein family, Gi and Gs), and compelling hypotheses for GPR126, an adhesion-type G-protein coupled receptor, providing a head-start for algorithm development and optimization. Coupling this approach to the fundamental experience in optimal control at the LTD, we aim to reveal potential driving forces that may guide and stabilize conformational transitions towards the end of the project.

蛋白质、RNA和DNA等大分子通过动态改变其三维结构来发挥细胞功能。了解这种分子的整体结构对于揭示它的关键作用以及潜在地干预恢复失去的功能至关重要。虽然传统的分子动力学可以提供原子详细的轨迹，但它们的计算成本仍然巨大，往往将分析限制在小的空间或时间尺度上。因此，较低的精度，但高通量的方法对于获得最初的见解和指导更详细的后续分析是有价值的，这些分析集中在感兴趣的领域。受机器人启发的运动学方法已成功地应用于粗粒度分子建模：它们的高效性质允许快速、可靠地洞察分子运动，并为数据解释和集成提供有用的工具。在这个提议中，我们的目标是将我们现有的运动学分子模拟软件扩展到一个统一的、力学-几何框架来研究复杂大分子的构象系综。我们将把原子相互作用的几何约束公式与刚性理论结合起来，以获得对分子柔性的一阶洞察。这一公式对分子施加了内在的运动层次，为在这样一个超高维环境中广泛而统一地采样构象空间和能量景观提供了有效的工具。通过立体或疏水接触的二面体自由度的较大变化、对强加约束的违反或非天然相互作用都可以报告对药物靶向重要的变构热点。研究几种蛋白质在两种主要构型之间的跃迁的初步令人兴奋的结果揭示了氨基酸的相互作用网络，这与已发表的实验数据非常一致。直接集成实验数据将有助于验证和改进我们的计算算法，并允许数据驱动的构象探索。由于实验数据往往稀疏且容易出错，我们的机械几何建模框架将有助于预测不可访问或未知的数据，并设计后续更有针对性的实验。我们将专注于来自传统和多温度结晶学、新一代X射线自由电子激光实验和双电子-电子共振(DER)的实验数据。我们已经有了几个系统的令人兴奋的数据(DJ-1超家族的异氰化物水合酶(ICH)和G蛋白家族的两个成员GI和Gs)，以及关于粘附型G蛋白偶联受体GPR126的令人信服的假设，为算法开发和优化提供了先机。将这种方法与LTD在最优控制方面的基本经验相结合，我们的目标是揭示潜在的驱动力，这些驱动力可能指导和稳定接近项目结束的构象转变。