Self-Correcting Distance Geometry for NMR Analysis and Protein Design

该项目将提高Werner Braun教授及其同事的新计算工具——用于核磁共振结构测定和蛋白质设计的自校正距离几何（SECODG）方法的稳定性、可靠性和效率。根据距离和二面角约束，用SECODG方法计算蛋白质、RNA或DNA的三维结构。与之前为一致数据集设计的距离几何方法不同，SECODG方法甚至可以从包含错误的约束集生成精确的结构。SECODG方法大大减少了从核磁共振数据生成蛋白质结构的时间。Braun博士的团队将进一步改进这种方法，并探索新的统计方法来检测错误。如果二级结构部分或完全已知，SECODG方法也可用于从氨基酸序列预测蛋白质的三级折叠。在一项初步研究中，它成功地通过氨基酸序列来预测小螺旋蛋白的折叠，现在它将被应用于更广泛的蛋白质折叠。SECODG方法将成为一个强大的计算工具，以加快从核磁共振数据确定大分子结构。实验确定的三维蛋白质结构是设计具有改进或新功能的新药或蛋白质的基础。结合能量最小化和蒙特卡罗模拟，它将有助于设计具有给定结构和新功能特性的蛋白质。

This project will enhance the stability, reliability and efficiency of Prof. Werner Braun and coworkers' new computational tool, the self-correcting distance geometry (SECODG) method for NMR structure determination and protein design. Three-dimensional structures of proteins, RNA or DNA are calculated by the SECODG method from distance and dihedral angle constraints. Unlike previous distance geometric methods, which were designed for consistent data sets, the SECODG method can generate accurate structures even from sets of constraints that contain errors. The SECODG method dramatically reduces the time to generate protein structures from NMR data. Dr. Braun's group will further improve this method and explore new statistical methods for error detection. The SECODG method can also be used to predict the tertiary folds of proteins from the amino acid sequence, if the secondary structure is partially or completely known. It was successfully tested in a preliminary study for its ability to predict the fold of small helical proteins from their amino acid sequences, and it will now be applied to a broader range of protein folds. The SECODG method will be a powerful computational tool to speed up macromolecular structure determination from NMR data. Experimentally determined three-dimensional protein structures are the basis to design new drugs or proteins with improved or new functions. In combination with energy minimization and Monte Carlo simulations it will help in designing proteins with given structural and new functional properties.