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

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.