PROJECT 2 - MODEL COMPLETION AND VALIDATION

作为PHENIX项目的一部分，我们开发了RESOLVE模型构建算法， 密度修饰程序的进展，并开发了柔性配体与电子的快速拟合 密度图我们与PHENIX项目的其他成员合作， PHENIX和开发适合于自动决策的结构确定向导。 我们建议使用PHENIX中的算法和软件的基础作为自动化的基础， 完成和验证大分子结构。与项目I合作，我们将产生 中等分辨率的高度完整的模型。与Project IV合作，我们将开发模板匹配 核酸建模技术。我们将创建一个多模型表示， 晶体结构中存在的大分子的构象。我们将开发我们的“完全省略”程序， 生成高度准确但不受原子模型影响的电子密度图，并将其用于 验证程序。我们将开发一种用于结构测定的两次通过程序， 一个初步的模型和阶段，然后是第二个全面的通过，根据以下方面的相关性做出决定： 阶段到模型阶段。我们将开发PHENIX解决方案对象作为记录所有 在结构确定期间进行的步骤，允许重复全面沉积。我们将 制定系统的程序，评估实验数据、地图和 模型我们将创建一个数据库的质量措施，决策及其结果，通过执行 使用PHENIX结构库确定结构，并与项目I、III和IV合作， 用于结构确定中的决策的机器学习算法。这项工作将使快速 高分子的高度完整模型的确定和沉积，对于 了解生物学和改善人类健康。

As part of the PHENIX project we have developed the RESOLVE model-building algorithm, made major advances in density modification procedures, and developed rapid fitting of flexible ligands to electron density maps. We have collaborated with other members of the PHENIX project integrating these algorithms into PHENIX and to developing structure determination Wizards suitable for automated decision-making. We propose to use this foundation of algorithms and software in PHENIX as a basis for automating the completion and validation of macromolecular structures. In collaboration with project I, we will generate highly complete models at moderate resolution. In collaboration with Project IV we will develop templatematching techniques for model-building of nucleic acids. We will create a multi-model representation of the conformations of a macromolecule present in a crystal structure. We will develop our "full-omit" procedure to generate electron density maps that are highly accurate but unbiased by atomic models, and use them in validation procedures. We will develop a two-pass procedure for structure determination, a rapid pass to get a preliminary model and phases, then a second thorough pass making decisions based on correlations of phases to the model phases. We will develop PHENIX Solution objects as a framework for recording all steps carried out during structure determination, allowing repetition comprehensive deposition. We will develop systematic procedures for assessing the quality and characteristics of experimental data, maps and models. We will create a database of quality measures, decisions and their outcomes by carrying out structure determinations using PHENIX structure library and collaborate with Projects I, III and IV to develop machine-learning algorithms for decision-making in structure determination. This work will allow rapid determination and deposition of highly complete models of macromolecules and will be important for understanding biology and improving human health.