Applications of Real-Space Refinement for Macromolecular Structure Analysis

计算方法将开发用于测定，细化和解释大分子结构的x射线晶体学和电子显微镜。晶体结构的确定包括使用交互式计算机程序将原子模型手工构建为电子密度图，然后将模型自动细化到衍射振幅。晶体学应用将在模型构建和自动往复空间细化之间搭建桥梁，这一过渡是晶体学结构确定中的瓶颈和主要错误的常见来源。利用约束实空间细化的新技术，模型与电子密度的拟合将在放弃相位进行传统的往复空间细化之前得到优化。这种局部细化反过来又允许相位和映射得到改进，从而完成一个迭代循环，从而获得一种新的模型改进方法，该方法没有省略映射的反馈偏差。因此，新方法将具有广泛的科学影响，因为它们将为结构生物学实验室共同的计算机平台构建。新方法将增加可以完成的大分子结构测定的数量，提高质量，减少错误的机会，并减少所需的资源。因此，这项工作将有助于我们进一步了解单个大分子的功能，以及它们在许多细胞过程的核心大分子组装中的相互作用。该奖项由计算生物学活动和生物物理学项目支持。

Computational methods will be developed for the determination, refinement and interpretation of macromolecular structures by X-ray crystallography and electron microscopy. Crystallographic structure determination involves the manual building of atomic model into an electron density map, using interactive computer programs, followed by automatic refinement of the model to the diffraction amplitudes. The crystallographic applications will bridge the transition between model building and automatic reciprocal-space refinement, a transition that is a bottle-neck and a common source of major errors in crystallographic structure determination. Using new techniques of restrained real-space refinement, the fit of the model to the electron density will be optimized before the phases are abandoned for conventional reciprocal-space refinement Such local refinement, in turn allows the phases and map to be improved, completing an iterative cycle for a new method of model improvement that is free from the feedback bias of omit-maps. Thus the new methods will have broad scientific impact since they will be built for computer platforms common to the structural biology laboratories. The new methods will increase the number of macromolecular structure determinations that can be completed, improve the quality, decrease the chance of errors, and decrease the resources that are required. Thus, this work will help further our understanding of the function of individual macromolecules, and of their interactions in the macromolecular assemblies that are central to many cellular processes. This award is supported by the Computational Biology Activity and the Biophysics programs.