Bridging the gap between theory and experiment in paramagnetic NMR analysis
Lead Research Organisation:
University of Bath
Department Name: Chemistry
Abstract
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.
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.
Organisations
People |
ORCID iD |
| Elizaveta Suturina (Principal Investigator) |