Computational Tools for Neutron Protein Crystallography

中子蛋白质晶体学的计算工具

• 批准号: 9357657
• 负责人: Paul A Langan
• 金额: $ 36.23万
• 依托单位: UT-BATTELLE, LLC-OAK RIDGE NATIONAL LAB
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-08 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9357657
• 关键词: Accounting; Address; Algorithms; Automation; Automobile Driving; Binding; Biological; Cells; Communities; Complex; Computer software; Computing Methodologies; Crystallization; Crystallography; Data; Data Analyses; Detergents; Development; Dihydrofolate Reductase; Environment; Generations; Hydrogen; Knowledge; Lipids; Manuals; Maps; Membrane Proteins; Methods; Modeling; Modification; Nature; Neutron Diffraction; Neutrons; Noise; Nuclear; Pharmaceutical Preparations; Process; Property; Proteins; Public Health; Pythons; Research; Research Personnel; Roentgen Rays; Role; Series; Signal Transduction; Solvents; Structural Biologist; Structure; System; Techniques; Vision; Work; base; biological systems; carbonate dehydratase; complex biological systems; computerized data processing; computerized tools; density; experience; experimental study; improved; innovation; macromolecule; stereochemistry; success; tool

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

项目概要/摘要 该项目的目标是继续发明、完善和应用中子的通用计算工具 蛋白质晶体学。我们的主要重点是提供创新但实用的解决方案，这些解决方案可以 快速部署到目标用户社区，解决中子蛋白的计算瓶颈 晶体学并确定一系列蛋白质的中子结构，其中许多蛋白质具有高生物医学价值 重要性，跨越了一系列的规模和复杂性。 我们将继续当前的工作，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）用于自动晶体学作为可扩展的 C++ 和Python模块。该软件将使用基本编程工具并为其做出贡献 计算晶体学工具箱 (cctbx) 中的晶体学。我们的愿景是为 具有一系列晶体学经验的结构生物学家可以使用的计算工作台 交替用于中子、X 射线或整体中子/X 射线/能量晶体学。 迄今为止，我们的研究表明，用于分析中子衍射数据的计算方法 可以改进，具有更好的密度图、更好的模型、更高的自动化程度和更可靠的结构 细化。我们还表明，这些方法可以成功应用于具有挑战性的生物 系统。然而，随着新实验设施的投入使用，很明显，新型生物系统 将被研究。中子方法需要进一步改进才能在这些情况下取得成功。在我们的 与不断增长的中子晶体学用户社区的互动越来越清楚 仍然存在的挑战限制了这一领域的进展，需要创新的解决方案 本项目中提出。该项目开发的计算工具将用于改变当前的研究 到尺寸更大、复杂性和生物医学相关性更大的更复杂的结构。我们的新 发展将导致所有类别的中子衍射数据的模型得到改进，同时 使得研究新的具有挑战性的生物系统成为可能。 1