Hyper-dimensional NMR spectroscopy for automated protein structure determination
Final Report Abstract
The main objective of this project was to develop experimental and computational hyperdimensional NMR methods to achieve the maximally possible signal resolution in NMR spectra, which is only limited by physical properties of the biomolecules themselves, and not by sampling inefficiency of the experiments, to apply these techniques to large sets of NMR spectra, and subsequently, after interfacing its output to an automated resonance assignment algorithm, to perform fully automated protein structure calculations. The research on the hyper-dimensional NMR methodology prior to 2008 was mostly devoted to backbone assignment experiments. In this project the methodology was extended to side-chain chemical shifts assignments and to structure calculation. The resulting peak data are mostly unambiguously resolved, accurate, self-consistent, and tabulated (by spin-systems) peak data. For the assignments we performed joint interpretation of increasingly larger sets of experiments of 2 to 14 experiments that were recorded in non-uniform sampling mode. By hyper-dimensional processing we achieved optimizations of resolution versus sensitivity with respect to the available/given measurement time, and we obtained resolution enhancement in the corresponding dimensions of less sensitive experiments from the most sensitive ones. The methodology was implemented both on the local computer network of the Center for Biomolecular Magnetic Resonance (BMRZ) at the Goethe University Frankfurt, and on the high-performance We-NMR Grid as a portal that is accessible for the international community. We applied high-resolution spectroscopy and automated approaches to several proteins of varying molecular sizes, including rapidly decaying samples of cell lysates. The work demonstrated the power of the method for structural biology in the ability to quickly study biological macromolecules in solutions composed of all native cell ingredients.
Publications
- Analysis of non-uniformly sampled spectra with multidimensional decomposition. Progress NMR Spectrosc. 59, 271-292 (2011)
Orekhov, V.Y., Jaravine, V.A.
- A universal expression tag for structural and functional studies of proteins. ChemBioChem 13, 959–963 (2012)
Rogov, V. V., Rozenknop, A., Rogova, N. Y., Löhr, F., Tikole, S., Jaravine, V., Güntert, P., Dikic, I. & Dötsch, V.
(See online at https://doi.org/10.1002/cbic.201200045) - Blind testing of routine, fully automated determination of protein structures from NMR data. Structure 20, 227–236 (2012)
Rosato, A., Aramini, J. M., Arrowsmith, C., Bagaria, A., Baker, D., Cavalli, A., Doreleijers, J. F., Eletsky, A., Giachetti, A., Guerry, P., Gutmanas, A., Güntert, P., He. Y. F., Herrmann, T., Huang, Y. J., Jaravine, V., Jonker, H. R. A., Kennedy, M. A., Lange, O. F., Liu, G., Malliavin, T. E., Mani, R., Mao, B., Montelione, G. T., Nilges, M., Rossi, P., van der Schot, G., Schwalbe, H., Szyperski, T., Vendruscolo, M., Vernon, R., Vranken, W. F., de Vries, S., Vuister, G. W., Wu, B., Yang, Y. & Bonvin, A. M. J. J.
(See online at https://doi.org/10.1016/j.str.2012.01.002) - Fast automated NMR spectroscopy of short-lived biological samples. ChemBioChem 13, 964–967 (2012)
Tikole, S., Jaravine, V., Rogov, V. V., Rozenknop, A., Schmöe, K., Löhr, F., Dötsch, V. & Güntert, P.
(See online at https://doi.org/10.1002/cbic.201200044) - Protein structure validation by generalized linear model RMSD prediction. Protein Sci. 21, 229–238 (2012)
Bagaria, A., Jaravine, V., Huang, Y. J., Montelione, G. T. & Güntert, P.
