Computational Tools for Neutron Protein Crystallography

项目总结/摘要 这个项目的目的是继续发明、改进和应用中子的通用计算工具 蛋白质晶体学我们的主要重点将是提供创新的，但实际的解决方案， 快速部署到目标用户社区，以解决中子蛋白质的计算瓶颈 晶体学和确定一系列蛋白质的中子结构，许多具有高生物医学 重要性，它跨越了规模和复杂性的范围。 我们将继续我们目前的工作，调整这些计算工具，以纳入PHENIX（Python- 基于集成晶体学的分层环境）作为可扩展的C++ Python模块该软件将使用并有助于基本的编程工具， 计算晶体学（Computational Crystallographic，CCTBX）我们的愿景是为 一个计算工作台，具有一系列晶体学经验的结构生物学家可以使用 或者用于中子、X射线或全局中子/X射线/能量晶体学。 迄今为止，我们的研究表明，分析中子衍射数据的计算方法 可以通过更好的密度图、更好的模型、更高的自动化和更可靠的结构来改进 精致。我们还表明，这些方法可以成功地应用于挑战生物学 系统.然而，随着新的实验设施的上线，很明显，新的生物系统类别 将被研究。中子方法需要进一步发展，才能在这些情况下取得成功。在我们 与不断增长的中子晶体学用户社区的互动越来越清楚， 还有一些挑战限制了这一领域的进展，需要创新的解决方案 在这个项目中提出。在这个项目中开发的计算工具将被用来改变目前的研究 到更复杂的结构，这些结构在尺寸、复杂性和生物医学相关性上更大。我们的新 发展将导致改进的模型，从所有类别的中子衍射数据，同时 使研究新的具有挑战性的生物系统成为可能。 1

Project Summary/Abstract The aim of this project is to continue to invent, refine and apply generalizable computational tools for neutron protein crystallography. Our primary focus will be to provide innovative, but practical solutions, that can be rapidly deployed to the target user community to address the computational bottleneck in neutron protein crystallography and to determine the neutron structures of a series of proteins, many with high biomedical importance, that span a spectrum of size and complexity. We will continue our current work adapting these computational tools for incorporation into PHENIX (Python- based Hierarchical Environment for Integrated Xtallography) for automated crystallography as extensible C++ and Python modules. The software will use and contribute towards the basic programming tools for crystallography in the Computational Crystallographic Toolbox (cctbx). Our vision is to contribute to a computational workbench that structural biologist, with a range of crystallographic experience, can use alternately for neutron, X-ray, or global neutron/X-ray/energy crystallography. Our research so far has demonstrated that the computational methods for analysis of neutron diffraction data can be improved, with better density maps, better models, increased automation and more reliable structure refinement. We have also shown that these methods can be successfully applied to challenging biological systems. However, as new experimental facilities come on line it is clear that new classes of biological systems will be studied. Neutron methods will need to be further advanced for success in these cases. In our interactions with the growing neutron crystallography user community it is becoming increasingly clear that there are remaining challenges that limit progress in this field and that will require the innovative solutions proposed in this project. The computational tools developed in this project will be used to shift current research to more complex structures that are larger in size, complexity and biomedical relevance. Our new developments will lead to improved models from all classes of neutron diffraction data, while simultaneously making it possible to study new challenging biological systems. 1