Automated NMR Assignment and Protein Structure

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长期、困难和昂贵的任务。高通量的结构基因组学是必需的，以便应用现代技术，如基于结构的药物设计在更大的规模。传统的（半）自动化方法通过核磁共振（NMR）光谱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。利用核磁共振测定蛋白质三维结构的主要瓶颈之一是生物聚合物中原子的化学位移。因此，使用NMR的高通量结构测定需要对分配问题进行系统攻击。新的算法技术提出了自动分配和蛋白质结构测定稀疏，未分配的NMR数据，基于一种称为拼图的方法。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。为了实现高通量的数据收集，所提出的方法只利用一些快速，廉价的NMIR实验。该研究将建立在Jigsaw的基础上，开发一种最低限度的方法，展示在一些关键光谱中可用的大量信息，以及如何使用组合和几何算法的组合来提取这些信息。新的算法和计算机系统将被开发用于仅从四个NMR谱确定蛋白质结构。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。Jigsaw将扩展到更大的蛋白质，并在实验NMR数据上进行测试。一个新的概率框架将实施处理增加的光谱复杂性和稀疏的信息内容获得较大的蛋白质，并在高通量NMR协议。

DESCRIPTION (provided by applicant): While automation is revolutionizing many aspects of biology, the determination of three-dimensional protein structure remains a long, hard, and expensive task. High-throughput structural genomics is required in order to apply modem techniques such as structure-based drug design on a much larger scale. Traditional (semi-) automated 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. One of the main bottlenecks in the determination of three-dimensional protein structures by NMR is the assignment of chemical shifts to atoms in a biopolymer. Therefore, high-throughput structure determination using NMR requires a systematic attack on the assignment problem. Novel algorithmic techniques are proposed for automated assignment and protein structure determination from sparse, unassigned NMR data, based on an approach called Jigsaw. 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. To enable high-throughput data collection, the proposed methods utilize only a few fast, cheap NMIR experiments. The research will build on Jigsaw to develop a minimalist approach, demonstrating the large amount of information available in a few key spectra, and how it can be extracted using a combination of combinatorial and geometric algorithms. New algorithms and computer systems will be developed for determining protein structure from only four 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. Jigsaw will be extended to work on larger proteins, and tested on experimental NMR data. A novel probabilistic framework will be implemented to handle the increased spectral complexity and sparser information content obtained both for larger proteins, and in high-throughput NMR protocols.