Computational Tools for Neutron Protein Crystallography

项目摘要/摘要 这个项目的目标是继续发明、改进和应用可推广的中子计算工具 蛋白质结晶学。我们的主要重点将是提供创新但实用的解决方案，即 快速部署到目标用户社区，以解决中子蛋白质的计算瓶颈 结晶学和确定一系列蛋白质的中子结构，其中许多具有很高的生物医学价值 重要性，这跨越了规模和复杂性的范围。 我们将继续我们目前的工作，将这些计算工具整合到PHENIX(Python- 基于集成Xtallograph的层次化环境)作为可扩展C++的自动化结晶学 和Python模块。该软件将使用并促进以下基本编程工具 在计算晶体工具箱(Cctbx)中的结晶学。我们的愿景是为 结构生物学家，具有广泛的结晶学经验，可以使用的计算工作台 可交替用于中子、X射线或全球中子/X射线/能量结晶学。 到目前为止，我们的研究已经证明，分析中子衍射数据的计算方法 可以改进，具有更好的密度地图、更好的模型、更高的自动化程度和更可靠的结构 精致。我们还表明，这些方法可以成功地应用于挑战生物 系统。然而，随着新的实验设备的上线，很明显，新的生物系统类别 将会被研究。要在这些情况下取得成功，中子方法还需要进一步发展。在我们的 与不断增长的中子结晶学用户社区的互动越来越明显 仍然存在限制这一领域进展的挑战，这将需要创新的解决方案 在这个项目中提出的。在这个项目中开发的计算工具将用于转移当前的研究 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都更大。我们的新产品 发展将导致所有类别的中子衍射数据的改进模型，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