Periodic Reporting for period 1 - OPTIMAL NMR (Optimal control methods for biological solid state nuclear magnetic resonance)
Periodo di rendicontazione: 2015-09-01 al 2017-08-31
Solid state nuclear magnetic resonance (ssNMR) is an experimental method that allows structure elucidation of macromolecules. As part of the structural biology toolbox, its results contribute to our understanding of basics of life and it assists in the search for effective drugs. The project OPTIMAL-NMR aimed at improving quality and sensitivity of ssNMR measurements using advanced experimental methods developed by means of optimal control theory. Such methods have been suggested in the past but have not been used, despite their predicted benefits. The OPTIMAL-NMR project systematically evaluated possible reasons why ssNMR optimal control (OC) experiments performed only poorly compared to idealized numerical simulations. It was found that it is due to temporal variations in the excitation field induced by sample rotation in a spatially inhomogeneous field of the excitation coil. The project concluded with a recipe how to develop robust OC experiments that can compensate for such complications. Using a specific example of ssNMR experiment on protein samples, an improvement over 50% in signal-to-noise ratio compared to conventional methods has been demonstrated. This result leads to an increased accessibility of structural information from the acquired ssNMR spectra and contributes to progress in structural biology.
Regarding the scientific part of the project, the researcher investigated transient effects in radiofrequency (RF) circuits of the NMR hardware and their influence on performance of experiments developed by means of optimal control. In the next step, a detailed analysis of RF field distribution in ssNMR probes was performed and found to have the major impact. A numerical optimization protocol was developed and applied to improve an experiment for magnetization transfer between nitrogen and carbon nuclei of a solid protein sample. The researcher contributed to ssNMR studies of proteins involving the latest ultrafast magic angle spinning experiments by performing novel large scale numerical simulations of proton spectra of a protein. This part of the project resulted in two scientific papers that are already published and two manuscripts that are in preparation.
During the secondment in a company manufacturing the NMR hardware, the researcher used numerical spin dynamics calculations to predict performance of ssNMR experiments on probeheads with different RF coil designs. These predictions are currently being tested in practice and, if successful, may result in a new product.
This Marie Sklodowska-Curie Individual Fellowship project also aims to promote the researcher’s carrier through enhancing his visibility within the research community. Besides participating on international scientific conferences, the researcher was invited as a lecturer to 2 international ssNMR schools and one workshop, involving together approximately 100 students.
During the secondment in a company manufacturing the NMR hardware, the researcher used numerical spin dynamics calculations to predict performance of ssNMR experiments on probeheads with different RF coil designs. These predictions are currently being tested in practice and, if successful, may result in a new product.
This Marie Sklodowska-Curie Individual Fellowship project also aims to promote the researcher’s carrier through enhancing his visibility within the research community. Besides participating on international scientific conferences, the researcher was invited as a lecturer to 2 international ssNMR schools and one workshop, involving together approximately 100 students.
Solid state NMR spectroscopy is able to solve structures of insoluble proteins and their aggregates. The OPTIMAL-NMR project developed improved experimental schemes that bring this method closer to routine applications, the necessary step towards efficient development of new drugs for diseases such as the Alzheimer’s disease. An advanced large-scale numerical optimization protocol was developed and resulted in a new experimental method providing 50% increase of sensitivity compared to the state of the art methods, without the need for a new hardware.