NMRFx: An Integrated software suite for macromolecular NMR analysis

了解蛋白质和核酸的结构、动力学和配体结合特异性对于 了解人类疾病的机制，以及可以干预的分子的最佳设计， 治疗疾病的过程。核磁共振（NMR）光谱是最重要的 获得这种分子信息的通用技术。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 继续开发并添加强大的新功能， NMR数据的计算分析。拥有这种有效的NMR软件，NMRFx，尤其重要， 我们意识到，对大分子结构和功能的深入了解并不来自于单一的技术， NMR，但从补充信息，从各种技术，包括NMR，X射线晶体学， 和冷冻电子显微镜。科学家们不再把这些技术视为专门技术 只有领域专家才能使用，但是作为一个技术的集合，可以并且应该一起应用。 如果没有这个NMRFx软件，研究人员必须使用来自不同实验室的各种工具， 不同的编程语言、脚本工具、命名约定、图形界面、文档 作为本项目主题的NMRFx软件集成了信号处理，数据分析， 可视化和基于NMR数据的大分子结构计算。在目前的项目中， 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 使用这些新工具进行数据分析，使科学研究取得重大进展。性能 软件的性能将通过增加一个软件库而得到极大的增强，该软件库允许计算 被分派到GPU（图形处理单元）和FPGA（现场总线）等高性能硬件上 可编程门阵列）。这种性能增强将允许更快的计算，并允许 使用以前在交互式分析中速度太慢的算法。我们亦会完成 实现NMR数据的全面分析所需的其余功能。我们建议， 为新用户和有经验的用户提供我们软件应用程序的增强版本， 安装协议、界面风格、数据结构和整体设计，我们可以大大降低障碍， 使用NMR。NMRFx使NMR技术的使用更容易为更广泛的人群所接受， 研究人员软件，包括深度学习和加速库的增加， NMR在影响人类健康的广泛生物研究领域的贡献。

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.