Bridging the gap between theory and experiment in paramagnetic NMR analysis

弥合顺磁 NMR 分析理论与实验之间的差距

• 批准号: EP/W022028/1
• 负责人: Elizaveta Suturina
• 金额: $ 35.26万
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW022028%2F1
• 关键词: Bridging; gap; between; theory; experiment

The goal of the proposed research project is to innovate the paramagnetic NMR data analysis by integrating high-level quantum chemistry, improved models of pNMR shift and relaxation and machine learning. The proposed benchmark study would be able to define accuracy of different approaches tailored to range of systems. The employment of machine learning in this project would make a step forward to the automation of pNMR data analysis applied to wide variety of molecules.In the modern world, the biggest challenges often appear in the smallest scale of a single molecule or even a single atom. Hence, obtaining information about systems on that scale becoming more and more important. There are number of analytical techniques, such as X-Ray diffraction, that allow us to get a glimpse of molecular structure but majority of them require samples in the solid state. Identification of molecular structure and its dynamic properties in solution is often done by Nuclear Magnetic Resonance (NMR) spectroscopy. Advances in NMR data analysis for simple diamagnetic organic molecules have reached a milestone of a complete automation several years ago, when major NMR software producers released their Computer-Assisted Structure Elucidation (CASE) products. Now, with a hit of a button a user can assign all the peaks in the NMR spectra and get a molecular structure of species in the sample. When it comes to paramagnetic molecules or so-called pNMR, the contrast in analytic capabilities is striking. There are still debates in the scientific community on what equations describe pNMR peaks positions and linewidths. pNMR data analysis is currently at a similar stage as X-Ray diffraction crystallography in the beginning of XX century, when characterisation of one molecule could be published as a separate manuscript. Yet, paramagnetic molecules form an important class of compounds. In particular, paramagnetic metal complexes, which are employed as tags for protein structure and dynamics elucidation - information that forms the basis for modern drug design. They are also used as contrast agents or property responsive probes for magnetic resonance diagnostics.

拟议研究项目的目标是通过集成高级量子化学、改进的 pNMR 位移和弛豫模型以及机器学习来创新顺磁 NMR 数据分析。拟议的基准研究将能够定义针对一系列系统定制的不同方法的准确性。该项目中机器学习的应用将使 pNMR 数据分析的自动化向前迈进一步，应用于各种分子。在现代世界，最大的挑战往往出现在单个分子甚至单个原子的最小尺度上。因此，获取有关这种规模的系统的信息变得越来越重要。有许多分析技术（例如 X 射线衍射）可以让我们了解分子结构，但大多数分析技术都需要固态样品。分子结构及其在溶液中的动态特性的鉴定通常通过核磁共振 (NMR) 光谱来完成。几年前，当主要 NMR 软件生产商发布了计算机辅助结构解析 (CASE) 产品时，简单抗磁性有机分子的 NMR 数据分析的进展已经达到了完全自动化的里程碑。现在，只需按一下按钮，用户就可以分配 NMR 谱中的所有峰，并获得样品中物质的分子结构。当谈到顺磁性分子或所谓的 pNMR 时，分析能力的对比是惊人的。科学界对于什么方程描述 pNMR 峰位置和线宽仍然存在争论。 pNMR 数据分析目前正处于与 20 世纪初期的 X 射线衍射晶体学相似的阶段，当时一个分子的表征可以作为单独的手稿发表。然而，顺磁性分子形成了一类重要的化合物。特别是顺磁性金属配合物，它被用作蛋白质结构和动力学阐明的标签，这些信息构成了现代药物设计的基础。它们还用作磁共振诊断的造影剂或性能响应探针。