Automated NMR Assignment and Protein Structure Determination

自动 NMR 分配和蛋白质结构测定

• 批准号: 7940504
• 负责人: Bruce R. Donald
• 金额: $ 26.25万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7940504
• 关键词: Address; Algorithms; Area; Automation; Back; Binding Proteins; Biology; Biomolecular Nuclear Magnetic Resonance; Biopolymers; Chemicals; Classification; Complex; Computer software; Computing Methodologies; Data; Data Quality; Detection; Disease; Drug Design; Goals; Grant; Homo; Homologous Gene; Homologous Protein; Homology Modeling; Human; Maps; Membrane; Membrane Proteins; Methods; Molecular; Molecular Conformation; NMR Spectroscopy; Nuclear; Nuclear Magnetic Resonance; Phase; Plant Roots; Procedures; Progress Reports; Proteins; Proteomics; Relaxation; Research; Residual state; Software Tools; Solutions; Structural Models; Structure; Techniques; Testing; Therapeutic Intervention; Time; Uncertainty; Vertebral column; X-Ray Crystallography; base; bone; database structure; design; improved; monomer; novel; open source; programs; protein complex; protein folding; protein protein interaction; protein structure; research study; restraint; structural biology; tool; vector

DESCRIPTION (provided by applicant): While automation is revolutionizing many aspects of biology, the determination of three-dimensional (3D) protein structure remains a long, hard, and expensive task. Novel algorithms and computational methods in biomolecular NMR are necessary to apply modern techniques such as structure-based drug design and structural proteomics on a much larger scale. Traditional (semi-) automated approaches to protein structure determination through NMR spectroscopy require a large number of experiments and substantial spectrometer time, making them dif - cult to fully automate. A chief bottleneck in the determination of 3D protein structures by NMR is the assignment of chemical shifts and nuclear Overhauser effect (NOE) restraints in a biopolymer. Therefore, we propose a novel attack on the assignment problem, to enable high-throughput NMR structure determination. Similarly, it is difficult to determine protein structures accurately using only sparse data. Sparse data arises not only in high-throughput settings, but also for larger proteins, membrane proteins, and symmetric protein complexes. New algorithms will be implemented to handle the increased spectral complexity and sparser information content obtained for such difficult proteins. The proposed research aims to minimize the number and types of NMR experiments that must be performed and the amount of human effort required to interpret the experimental results, while still producing an accurate analysis of the protein structure. The long-term goal of our project is to address key computational bottlenecks in NMR structural biology. In the past grant period, we have reported progress in automated assignments, novel algorithms for protein structure determination, characterization of protein complexes and membrane proteins, and fold recognition using only unassigned NMR data. We will develop novel geometric algorithms to improve and extend these techniques, focusing on four key areas: (a) Nuclear Vector Replacement (NVR), a molecular replacement-like technique for structure-based assignment; (b) sparse-data algorithms for protein structure determination from residual dipolar couplings (RDCs) using exact solutions and systematic search; (c) structure determination of membrane proteins and complexes, especially symmetric oligomers; and (d) automated assignment of NOE restraints in both monomers and complexes. We will develop and extend the software tools above in a set of integrated programs for automated fold recognition, assignment, monomeric and oligomeric structure determination. All programs will be tested on experimental NMR data, and new structures will be determined using our algorithms. Project Narrative While automation is revolutionizing many aspects of biology, the determination of three-dimensional protein structure remains a long, hard, and expensive task. Determination of protein structures by nuclear magnetic resonance (NMR) is valuable in many biomedical applications such as structure-based drug design. Since structural studies of proteins can not only provide clues to disease causes but also provide a basis for the rational design of therapeutic interventions, we propose novel algorithms and computational methods in biomolecular NMR, which are necessary to apply modern techniques such as structure-based drug design and structural proteomics on a much larger scale.

描述（由申请人提供）：虽然自动化正在彻底改变生物学的许多方面，但三维（3D）蛋白质结构的测定仍然是一项漫长、艰巨且昂贵的任务。生物分子核磁共振中的新算法和计算方法对于更大规模地应用现代技术（例如基于结构的药物设计和结构蛋白质组学）是必要的。通过核磁共振波谱法测定蛋白质结构的传统（半）自动化方法需要大量的实验和大量的波谱仪时间，因此很难完全自动化。通过 NMR 测定 3D 蛋白质结构的主要瓶颈是生物聚合物中化学位移和核奥弗豪瑟效应 (NOE) 限制的分配。因此，我们提出了一种针对分配问题的新方法，以实现高通量 NMR 结构测定。同样，仅使用稀疏数据也很难准确确定蛋白质结构。稀疏数据不仅出现在高通量环境中，而且也出现在较大的蛋白质、膜蛋白和对称蛋白质复合物中。将实施新的算法来处理此类困难蛋白质增加的光谱复杂性和稀疏的信息内容。拟议的研究旨在最大限度地减少必须进行的核磁共振实验的数量和类型以及解释实验结果所需的人力，同时仍然对蛋白质结构进行准确的分析。我们项目的长期目标是解决 NMR 结构生物学中的关键计算瓶颈。在过去的资助期内，我们报告了自动分配、蛋白质结构测定的新算法、蛋白质复合物和膜蛋白的表征以及仅使用未分配的 NMR 数据进行折叠识别方面的进展。我们将开发新颖的几何算法来改进和扩展这些技术，重点关注四个关键领域：（a）核向量替换（NVR），一种用于基于结构的分配的类似分子替换的技术； (b) 使用精确解和系统搜索从残余偶极耦合（RDC）确定蛋白质结构的稀疏数据算法； (c) 膜蛋白和复合物，特别是对称寡聚物的结构测定； (d) 自动分配单体和复合物中的 NOE 限制。我们将在一套集成程序中开发和扩展上述软件工具，用于自动折叠识别、分配、单体和寡聚结构测定。所有程序都将在实验核磁共振数据上进行测试，并将使用我们的算法确定新结构。 项目叙述 虽然自动化正在彻底改变生物学的许多方面，但三维蛋白质结构的测定仍然是一项漫长、艰巨且昂贵的任务。通过核磁共振 (NMR) 确定蛋白质结构在许多生物医学应用中很有价值，例如基于结构的药物设计。由于蛋白质的结构研究不仅可以为疾病原因提供线索，还可以为合理设计治疗干预措施提供基础，因此我们在生物分子核磁共振中提出了新的算法和计算方法，这对于应用基于结构的药物等现代技术是必要的更大规模的设计和结构蛋白质组学。