NMRFx: An Integrated software suite for macromolecular NMR analysis

了解蛋白质和核酸的结构、动力学和配基结合特异性对于 了解人类疾病的机制，以及能够干预的分子的最佳设计 在疾病的治疗过程中。核磁共振波谱是其中最重要的技术之一。 获得这种分子信息的多种技术。核磁共振在生物医学中的巨大价值 科学只有通过强大的计算工具的应用才能实现。我们在这个项目中的目标是 继续开发用于的集成软件应用程序，并为其添加强大的新功能 核磁共振数据的计算分析。拥有这个有效的核磁共振软件NMRFx尤其关键，因为 我们意识到，对大分子结构和功能的深入了解不是来自于像这样的单一技术 核磁共振，但来自各种技术的补充信息，包括核磁共振，X射线结晶学， 和低温电子显微镜。科学家不再认为这些技术是专门的技术 只能由领域专家使用，但作为可以且应该一起应用的技术的集合。 如果没有NMRFx软件，研究人员必须使用来自不同实验室的各种工具，这些实验室具有 不同的编程语言、脚本工具、命名约定、图形界面、文档 NMRFx软件是本项目的主题，它集成了信号处理、数据分析、 基于核磁共振数据的可视化和大分子结构计算。在当前项目中，此 软件将通过添加集成的深度学习库进行增强，该库将允许新类型的 使用这些在科学研究方面取得重大进展的新工具进行数据分析。演出 通过添加允许计算的软件库，软件的性能将大大增强 派送至高性能硬件，如图形处理器(GPU)和现场可编程门阵列(FGA 可编程门阵列)。这一性能增强将允许更快的计算，并允许 使用以前太慢而不能在交互分析中使用的算法。我们还将完成 实施全面分析核磁共振数据所需的其余功能。我们建议通过 为新用户和有经验的用户提供我们的软件应用程序的增强版本，该应用程序具有 安装协议、界面风格、数据结构和整体设计，我们可以大幅降低门槛 核磁共振的使用。NMRFx正在使更广泛的人群更容易使用核磁共振技术 研究人员。软件，包括增加深度学习和加速库，将增加 核磁共振在影响人类健康的广泛生物学研究领域的贡献。

Knowing the structure, dynamics and ligand binding specificity of proteins and nucleic acids is essential to understanding the mechanisms of human disease and to the optimal design of molecules that can intervene therapeutically in disease processes. Nuclear Magnetic Resonance (NMR) Spectroscopy is one of the most versatile techniques for obtaining this molecular information. This tremendous value of NMR in biomedical science is only realized through the application of powerful computational tools. Our goal in this project is to continue the development of, and add powerful new features to, an integrated software application for the computational analysis of NMR data. Having this effective software, NMRFx, for NMR is especially critical as we realize that deep insight into macromolecular structure and function comes not from a single technique like NMR, but from the complementary information from various techniques including NMR, X-ray crystallography, and Cryo-electron microscopy. Scientists are no longer seeing these techniques as specialized techniques usable by only domain experts, but as a collection of techniques that can and should be applied together. Without this NMRFx software, researchers must otherwise use a variety of tools from different labs that have different programming languages, scripting tools, naming conventions, graphical interfaces, documentation styles etc. The NMRFx software that is the subject of this project integrates signal processing, data analysis, visualization and macromolecular structure calculations based on NMR data. In the current project this software will be enhanced with the addition of an integrated deep learning library that will allow new types of data analyses using these new tools that are making major advances in scientific research. The performance of the software will be dramatically enhanced by the addition of a software library that allows computations to be dispatched to high-performance hardware like GPUs (graphical processing units) and FPGAs (Field Programmable Gate Arrays). This performance enhancement will allow much faster computations and allow use of algorithms that were previously too slow to use in interactive analyses. We will also complete the implementation of the remaining features necessary for the full analysis of NMR data. We propose that by providing new and experienced users with an enhanced version of our software application that has a common installation protocol, interface style, data structures, and overall design, we can substantially lower barriers to the use of NMR. NMRFx is making the use of NMR techniques more accessible to a wider population of researchers. The software, including the addition of the deep learning and acceleration libraries, will increase the contributions of NMR in a broad range of biological research areas that impact on human health.