Postdoctoral: Physical Geometric Algorithms and Systems for High-Throughput NMR Structural Biology

博士后：高通量核磁共振结构生物学的物理几何算法和系统

• 批准号: 0102710
• 负责人: Bruce Donald
• 金额: $ 6.6万
• 依托单位: Dartmouth College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2004-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0102710&HistoricalAwards=false
• 关键词: Postdoctoral; Physical; Geometric; Algorithms; Systems

0102710Donald, BruceDartmouth CollegeCISE Postdoctoral Associates in Experimental Computer Science: Physical Geometric Algorithms and Systems for High-Throughput NMRWhile automation is revolutionizing many aspects of biology, the determination of three-dimensional protein structure remains an expensive task. Traditional automated and semiautomated approaches to protein structure determination through nuclear magnetic resonance (NMR) spectroscopy require dozens of experiments and months of spectrometer time, making them unsuitable for high-throughput automation. The research proposed is to develop algorithms and systems for determining protein structure from only a few key NMR spectra. The system will use algorithms similar to and adapted from physical geometric algorithms, pattern recognition and machine vision, signal processing, and robotics, in order to analyze spectra, assign spectral peaks to atom interactions, compute secondary structure, and estimate the global fold. Previously developed software, JIGSAW, represents NMR data with graphs encoding potential interactions between amino acid residues. JIGSAW applies graph algorithms to find subgraphs encoding the secondary protein structure. The postdoctoral research associate will build on the insights of JIGSAW by assisting to 1) integrate automated analysis of geometry and correlations in three-dimensional input spectra, 2) prove correctness, completeness, and complexity results within a random graph formalism, 3) extend JIGSAW to larger proteins, and 4) utilize long-range interactions in order to estimate three-dimensional protein structure.

0102710唐纳德，布鲁斯达特茅斯学院CISE博士后助理实验计算机科学：物理几何算法和系统的高分辨率NMR虽然自动化是革命性的生物学的许多方面，三维蛋白质结构的测定仍然是一项昂贵的任务。 传统的自动化和半自动化方法通过核磁共振（NMR）光谱法测定蛋白质结构需要数十次实验和数月的光谱仪时间，使其不适合高通量自动化。 提出的研究是开发算法和系统来确定蛋白质结构，只有几个关键的NMR光谱。 该系统将使用类似于物理几何算法，模式识别和机器视觉，信号处理和机器人技术的算法，以分析光谱，将光谱峰分配给原子相互作用，计算二级结构，并估计全局折叠。 先前开发的软件JIGSAW用编码氨基酸残基之间潜在相互作用的图表示NMR数据。 JIGSAW应用图算法来找到编码二级蛋白质结构的子图。 博士后研究助理将建立在JIGSAW的见解，协助1）集成三维输入光谱中几何形状和相关性的自动分析，2）证明随机图形形式主义中的正确性，完整性和复杂性结果，3）将JIGSAW扩展到更大的蛋白质，以及4）利用远程相互作用来估计三维蛋白质结构。