Automated NMR Assignment and Protein Structure Determination

描述(申请人提供)：虽然自动化正在给生物学的许多方面带来革命性的变化，但确定三维(3D)蛋白质结构仍然是一项漫长、困难和昂贵的任务。为了在更大范围内应用基于结构的药物设计和结构蛋白质组学等现代技术，生物分子核磁共振中的新算法和计算方法是必要的。传统的(半自动)通过核磁共振光谱学确定蛋白质结构的方法需要大量的实验和大量的光谱分析时间，这使得它们很难完全自动化。核磁共振测定三维蛋白质结构的一个主要瓶颈是指定生物聚合物中的化学位移和核Overhauser效应(NOE)限制条件。因此，我们针对赋值问题提出了一种新的攻击方法，以实现高通量的核磁共振结构确定。同样，仅使用稀疏数据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，而且还出现在较大的蛋白质、膜蛋白质和对称蛋白质复合体中。将实施新的算法来处理这种困难蛋白质增加的光谱复杂性和更稀疏的信息量。这项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工作量，同时仍能产生对蛋白质结构的准确分析。我们项目的长期目标是解决核磁共振结构生物学中的关键计算瓶颈。在过去的资助期间，我们报告了在自动分配、蛋白质结构确定的新算法、蛋白质复合体和膜蛋白的表征以及仅使用未分配的核磁共振数据进行折叠识别方面的进展。我们将开发新的几何算法来改进和扩展这些技术，重点放在四个关键领域：(A)核载体替换(NVR)，一种基于结构分配的类似分子替换的技术；(B)使用精确解和系统搜索从剩余偶极偶极耦合(RDC)确定蛋白质结构的稀疏数据算法；(C)膜蛋白质和复合体，特别是对称低聚物的结构确定；以及(D)单体和复合体中NOE限制的自动分配。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、指定、单体和寡聚结构确定。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的结构。 项目叙事 虽然自动化正在给生物学的许多方面带来革命性的变化，但确定蛋白质的三维结构仍然是一项漫长、困难和昂贵的任务。利用核磁共振技术确定蛋白质结构在许多生物医学领域具有重要的应用价值，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提供疾病原因的线索，还可以为治疗干预措施的合理设计提供依据，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，这是在更大范围内应用基于结构的药物设计和结构蛋白质组学等现代技术所必需的。

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.