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.

虽然自动化正在彻底改变生物学的许多方面，但三维（3D）蛋白质结构的确定仍然是一项长期、困难和昂贵的任务。生物分子NMR中的新算法和计算方法对于更大规模地应用基于结构的药物设计和结构蛋白质组学等现代技术是必要的。通过NMR光谱法进行蛋白质结构测定的传统（半）自动化方法需要大量实验和大量光谱仪时间，使得它们难以完全自动化。利用核磁共振确定蛋白质三维结构的一个主要瓶颈是生物聚合物中化学位移和核奥弗豪泽效应（NOE）约束的分配。因此，我们提出了一种新的攻击分配问题，使高通量NMR结构测定。类似地，仅使用稀疏数据很难准确地确定蛋白质结构。稀疏数据不仅出现在高通量环境中，而且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增加的光谱复杂性和稀疏的信息内容获得这样的困难的蛋白质。这项研究旨在最大限度地减少必须进行的NMR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，蛋白质复合物和膜蛋白的表征，以及仅使用未分配的NMR数据的折叠识别的新算法的进展。我们将开发新的几何算法来改进和扩展这些技术，主要集中在四个关键领域：（a）核向量替换（NVR），一种基于结构的分子排列技术;（B）利用精确解和系统搜索从残余偶极偶联（RDC）确定蛋白质结构的稀疏数据算法;（c）膜蛋白和复合物的结构测定，特别是对称寡聚体;（d）单体和复合物中NOE限制的自动分配。我们将开发和扩展上述软件工具，在一套集成的程序，自动折叠识别，分配，单体和寡聚体结构的测定。所有程序都将在实验NMR数据上进行测试，并使用我们的算法确定新的结构。 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确定仍然是一项长期，艰巨和昂贵的任务。通过核磁共振（NMR）确定蛋白质结构在许多生物医学应用中是有价值的，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提供线索，疾病的原因，但也提供了一个合理的设计治疗干预措施的基础上，我们提出了新的算法和计算方法在生物分子NMR，这是必要的应用现代技术，如基于结构的药物设计和结构蛋白质组学在更大的规模。