Hierarchical Methods for Large BioMolecular Complexes

大型生物分子复合物的分层方法

• 批准号: 6916765
• 负责人: CHANDRAJIT L BAJAJ
• 金额: $ 23.94万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-01 至 2008-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6916765
• 关键词: RNA; X ray crystallography; chemical models; computer assisted sequence analysis; computer program /software; computer simulation; computer system design /evaluation; cryoelectron microscopy; macromolecule; molecular biology information system; molecular dynamics; molecular shape; nuclear magnetic resonance spectroscopy; protein structure function; structural biology; tomography

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.

描述（由申请人提供）：大型生物分子复合物（LBC）形成负责大多数生物过程的机制，并且与理解许多疾病（例如癌症和代谢紊乱）相关。对这些结构的了解不仅可以提供大分子如何在组装中发挥作用的机制描述，还可以为开发与疾病相关的治疗干预措施提供线索。如今，利用冷冻电子显微镜 (CryoEM)、电子断层扫描 (ET) 和 X 射线晶体学 (Xray) 或核磁共振波谱 (NMR) 的 LBC 混合实验方法需要辅以更快、更准确的计算处理以尽可能最佳的分辨率水平最终阐明 LBC 的超微结构。一旦重建了 LBC 的体积 CryoEM 图，该提案就解决了增强型和自动化计算处理流程的开发问题。我们特别建议开发分层计算表示、算法和软件，自动确定 LBC 的结构特征，并加速 LBC 与相关蛋白质和/或核酸之间的匹配和拟合技术。 更准确地说，我们的具体目标是： 目标 1：开发算法，用于从三种不同形态尺度的 Cryo-EM 图谱中确定 LBC 的结构特征。目标 2：开发用于生成已确定的 LBC 结构特征的分层体积样条近似的算法，以促进基于快速傅立叶的相关搜索方法。目标 3：使用我们的 LBC 体积样条近似开发快速相关搜索方法，包括结构特征识别、结构拟合、LBC 和蛋白质/RNA 对接。目标 4：实施、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。