Hierarchical Methods for Large BioMolecular Complexes

描述(由申请人提供)：大型生物分子复合体(LBC)形成了负责大多数生物过程的机制，并与了解许多疾病有关，如癌症和代谢紊乱。对这些结构的了解不仅将为大分子如何在组装中发挥作用提供机械描述，还将为开发与疾病相关的治疗干预提供线索。今天，利用冷冻电子显微镜(CryoEM)、电子断层扫描(ET)和X射线结晶学(Xray)或核磁共振(NMR)对LBC进行混合实验方法，需要有更快、更准确的计算处理，以尽可能高的分辨率最终阐明LBC的超微结构。这项建议涉及一旦重建了LBC的体积低温EM图，就开发一条增强的和自动化的计算处理管道。特别是，我们建议开发分层计算表示法、算法和软件，以自动确定LBC的结构特征，并加快LBC与相关蛋白质和/或核酸之间的匹配和拟合技术。 更确切地说，我们的具体目标是：目标1：开发从三种不同形态尺度的Cryo-EM图谱中确定LBC结构特征的算法。目的2：开发算法来生成确定的LBC结构特征的分层、体积样条近似，以促进基于傅立叶的快速相关搜索方法。目的3：利用我们的体积样条法建立快速相关搜索方法，包括结构特征识别、结构拟合、LBC和蛋白质/RNA对接。目标4：实现、测试、打包和自由分发我们的结构特征确定和快速搜索/拟合技术。

DESCRIPTION (provided by applicant): Large Biomolecular Complexes (LBCs) form the machinery responsible for most biological processes and are relevant to understanding many diseases such as cancer and metabolic disorders. Knowledge of these structures would provide not only the mechanistic descriptions for how macromolecules act in an assembly but also clues in developing therapeutic interventions related to disease. Today, hybrid experimental approaches for LBCs utilizing cryo-electron microscopy (CryoEM), electron tomography (ET) and X-ray crystallography (Xray) or nuclear magnetic resonance spectroscopy (NMR), need to be ably complimented with faster and more accurate computational processing for final ultrastructure elucidation of LBCs at the best level of resolution that is possible. This proposal addresses the development of an enhanced and automated computational processing pipeline, once a volumetric CryoEM map of an LBC has been reconstructed. In particular we propose the development of hierarchical computational representations, algorithms and software, which automates structural feature determination of LBCs as well as speeds up match and fitting techniques between LBCs and relevant proteins and/or nucleic acids. More precisely, our specific aims are: AIM 1: To develop algorithms for determining structural features of LBCs from Cryo-EM maps at three different morphological scales. AIM 2: To develop algorithms for generating hierarchical, volumetric spline approximations of the determined structural features of LBCs to facilitate fast Fourier based correlation search methods. AIM 3: To develop fast correlation search methods using our volumetric spline approximations for LBCs, including structural feature identification, structure fitting, LBC and protein/RNA docking. AIM 4: To implement, test, package and freely distribute our structural feature determination and fast search/fitting techniques.