NMRFx: An Integrated software suite for macromolecular NMR analysis

NMRFx：用于大分子 NMR 分析的集成软件套件

• 批准号: 10543487
• 负责人: Bruce A Johnson
• 金额: $ 32.34万
• 依托单位: ADVANCED SCIENCE RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10543487
• 关键词: Acceleration; Algorithms; Architecture; Area; Chemicals; Collection; Computer Analysis; Computer software; Cryoelectron Microscopy; Data; Data Analyses; Development; Disease; Documentation; Goals; Harvest; Health; Human; Java; Knowledge; Libraries; Ligand Binding; Linux; Machine Learning; Methods; Modernization; Molecular; Molecular Structure; NMR Spectroscopy; Names; Noise; Nuclear Magnetic Resonance; Nucleic Acids; Performance; Population; Positioning Attribute; Principal Investigator; Process; Programming Languages; Protein Analysis; Proteins; Protocols documentation; Research; Research Personnel; Running; Scheme; Science; Scientist; Source Code; Specificity; Speed; Structure; Techniques; Therapeutic; Time; Translating; Visualization; X-Ray Crystallography; biological research; cloud based; cloud platform; computerized data processing; computerized tools; computing resources; data exchange; data standards; deep learning; deep learning algorithm; design; experience; graphical user interface; human disease; insight; interest; magnetic field; molecular dynamics; new technology; novel strategies; open source; programs; rapid technique; signal processing; structural biology; symposium; time use; tool; usability; virtual machine

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.

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并优化设计可以干预的分子 在疾病过程中起到治疗作用。核磁共振 (NMR) 光谱是最常用的光谱技术之一 获得这种分子信息的多种技术。 NMR 在生物医学领域的巨大价值 科学只能通过强大的计算工具的应用来实现。我们这个项目的目标是 继续开发集成软件应用程序并为其添加强大的新功能 NMR 数据的计算分析。拥有这款有效的 NMR 软件 NMRFx 尤其重要，因为 我们意识到，对大分子结构和功能的深入了解并非来自像这样的单一技术 NMR，但来自各种技术的补充信息，包括 NMR、X 射线晶体学、 和冷冻电子显微镜。科学家不再将这些技术视为专门技术 只能由领域专家使用，但作为可以而且应该一起应用的技术集合。 如果没有这款 NMRFx 软件，研究人员就必须使用来自不同实验室的各种工具，这些工具具有 不同的编程语言、脚本工具、命名约定、图形界面、文档 作为该项目主题的 NMRFx 软件集成了信号处理、数据分析、 基于NMR数据的可视化和大分子结构计算。在当前项目中这个 软件将通过添加集成深度学习库得到增强，该库将允许新型 使用这些新工具进行数据分析正在科学研究中取得重大进展。性能 通过添加软件库，该软件的功能将得到显着增强，该库允许计算 被调度到高性能硬件，如 GPU（图形处理单元）和 FPGA（现场 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。我们还将完成 实现 NMR 数据全面分析所需的其余功能。我们建议通过 为新用户和有经验的用户提供具有共同点的增强版软件应用程序 安装协议、界面风格、数据结构、整体设计，可以大幅降低使用门槛 核磁共振的使用。 NMRFx 正在使 NMR 技术的使用变得更广泛 研究人员。该软件，包括添加深度学习和加速库，将会增加 核磁共振在影响人类健康的广泛生物研究领域中的贡献。