Combined Automatic Assignment of NMR Spectra and Calculation of Macromolecular Structure by Self-Correcting Distance Geometry

本计画将提升一种新的计算工具--自校正几何（SECODG）方法的稳定性、可靠性与效率，以结合核磁共振谱的指定与三维结构计算。 在DIAMOD/NOAH软件包中实现的SECODG方法大大减少了从NMR数据生成蛋白质结构的时间。 与以前的距离几何方法，这是专为一致的数据集，该方法可以生成精确的结构，从包含错误的约束集。 该项目将进一步改进该方法，使其适用于更大的蛋白质。 几个测试表明，本方法可以处理真实的数据，并显着快于人工交互式光谱解释方法。 第一个测试是比较NOESY光谱的自动结构计算结果，这些光谱是通过早期手动方法分析的，用于大小从40到135个氨基酸的6种蛋白质。 自动化方法分配了70-80%的NOESY交叉峰，并且三维结构具有相似的质量。 在第二个测试中，NOAH/DIAMOD使用先前未解释的NOESY和TOCSY光谱自动计算了一种同种型的crambin的3D结构束。 需要稍微修改和一些人工帮助，以便程序可以处理大量丢失的化学位移。 根据这一实际经验，该方法将得到改进，通过优化容错的目标函数，将灵敏度测试的约束，包括线形状信息的峰分配方法和接口的程序套件与程序自动顺序分配从其他组开发一个完全自动的计算包NMR光谱的直接解释。 该程序包将成为一个强有力的计算工具，以加速从NMR数据确定大分子结构。 实验确定的三维结构是设计具有改进或新功能的新药和蛋白质的基础。 结合能量最小化和蒙特卡罗模拟，它将有助于设计具有所需结构和新功能特性的蛋白质。

This project will enhance the stability, reliability and efficiency of a new computational tool, the self- correcting geometry (SECODG) method for combined assignment of NMR spectra and 3D structure calculation. The SECODG method implemented in the DIAMOD/NOAH software package dramatically reduces the time to generate protein structures from NMR data. Unlike previous distance geometry methods, which were designed for consistent data sets, the method can generate accurate structures from sets of constraints that contain errors. This project will further improve this method to be applicable for larger proteins. Several tests have demonstrated that the present method can deal with real data and is significantly faster than manual interactive spectral interpretation methods. The first test was to compare the results of automatic structure calculation from NOESY spectra that had been analyzed by earlier manual methods for 6 proteins ranging in size from 40 to 135 amino acids. The automated method assigned 70-80% of the NOESY cross peaks and the three-dimensional structures were of similar quality. In a second test, previously uninterpreted NOESY and TOCSY spectra were used by NOAH/DIAMOD to automatically calculate a 3D structure bundle for an isoform of crambin. Slight modifications and some manual assistance were required so that the program could deal with a significant number of missing chemical shifts. Based on this practical experience, the method will be improved by optimizing the error-tolerant target function, incorporating sensitivity tests for constraints, including line shape information in the peak assignment method and interfacing the program suite with programs for automated sequential assignment from other groups to develop a completely automatic computational package for direct interpretation of NMR spectra. This program package will be a powerful computational tool to speed up macromolecular structure determination from NMR da ta. Experimentally determined three-dimensional structures are the basis for designing new drugs and proteins with improved or new functions. In combination with energy minimization and Monte Carlo simulations, it will help in designing proteins with a desired structure and new functional properties.