Objective
Multidimensional nuclear magnetic resonance (nD NMR) plays a unique role in Science as a primary tool for the characterization of biomolecules, as part of drug-discovery processes, and in clinical imaging (MRI). Further progress in NMR is hampered by this spectroscopy s low sensitivity, arising from the weak interactions that it involves. The prospects of solving this problem by continuing with incremental bigger machines approaches are poor, given the high maturity reached by existing technologies. The present Project deals with this issue by departing from traditional concepts, and relying on two incipient but highly promising developments in the field. One of these pertains ex situ dynamic nuclear hyperpolarization, an approach capable of eliciting liquid state NMR signals that surpass those afforded by the highest-field spectrometers by factors e10,000. While capable of providing super-signals hyperpolarization has the drawback of involving irreversible changes in the physical state of the sample. This makes it incompatible with nD NMR technologies, requiring the collection of multiple scans identical to one another except for systematic delay variations. As second component in this high-risk/high-gain Project we propose merging hyperpolarization with "ultrafast" methods that we have recently developed for completing arbitrary nD NMR/MRI acquisitions within a single scan. The resulting synergy could increase sensitivity by orders of magnitude, while demanding negligibly small amounts of spectrometer/scanner time to complete nD acquisitions. This should provide an ideal starting point for the analysis of a variety of organic and structural biology problems, and provide new tools to explore in vivo metabolism focusing on cancer biomarkers.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesbiological sciencesbiochemistrybiomolecules
- medical and health sciencesclinical medicineoncology
- natural sciencesbiological sciencesmolecular biologystructural biology
- natural sciencesphysical sciencesopticsspectroscopy
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Call for proposal
ERC-2009-AdG
See other projects for this call
Funding Scheme
ERC-AG - ERC Advanced GrantHost institution
7610001 Rehovot
Israel