Open Access

Lena Buchner

• The knowledge of three-dimensional structures of biomolecules is fundamental for the understanding of their function. Nuclear magnetic resonance (NMR) spectroscopy represents besides X-ray crystallography one of the two most widely used techniques to study macromolecules at atomic resolution. Its application has long been a laborious task that could take months and required the expertise of an experienced scientist, however, owing to the tremendous effort that has been put into the development of respective computer algorithms, structure determination by NMR spectroscopy of small- to medium sized proteins is nowadays routinely performed. CYANA is one widely used software package, which combines the majority of individual steps towards a three-dimensional structure. The most common application of the program, however, restricts to the combined automated NOE assignment and structure calculation based on NOESY peak lists and an existing chemical shift assignment. Completely automated structure determination starting from NMR spectra is to date technically possible with CYANA, however, not yet routinely applied. In order to achieve this long-term goal, the individual steps need to become more robust with regard to data imperfections such as peak overlap, spectral artifacts or a limited amount of NMR data. The work presented in this thesis should be placed within the context of increasing the reliability and improving the accuracy of structures determined by CYANA on the basis of solution- as well as solid-state NMR data. The chapter “Systematic evaluation of combined automated NOE assignment and structure calculation with CYANA” comprises an extensive study on the robustness of the combined automated NOE assignment and structure calculation algorithm based on experimental solution NMR data sets that were modified in multiple ways to mimic different kinds of data imperfections. The results show that the algorithm is remarkably robust with regard to imperfections of the NOESY peak lists and the chemical shift tolerances but susceptible to lacking or erroneous resonance assignments, in particular for nuclei that are involved in many NOESY cross peaks. In the chapter “Peakmatch – A simple and robust tool for peaklist matching” a method to achieve self-consistency of the chemical shift referencing among a set of peak lists is presented. The Peakmatch algorithm matches a set of peak lists to a specified reference peak list, neither of which have to be assigned, by optimizing an assignment-free match-score function. The algorithm has been extensively tested on the basis of experimental NMR data sets of five different proteins. The results show that peak lists from many different types of spectra can be matched reliably as long as they contain at least two corresponding dimensions. NMR structures are represented by bundles of conformers whose spread indicates the precision of the atomic coordinates. However, there is as yet no reliable measure of structural accuracy, i.e. how close NMR conformers are to the “true” structure. Instead, the precision of structure bundles is widely (mis)interpreted as a measure of structural quality. Attempts to increase the precision thus often yield tight structure bundles where the precision overestimates the accuracy. To overcome this problem, the chapter “Increased reliability of NMR protein structures by consensus structure bundles” introduces a new protocol for NMR structure determination with the software package CYANA that produces bundles of conformers with a realistic precision that is throughout a large number of test data sets a much better estimate of the structural accuracy than the precision of conventional structure bundles. Solid-state NMR is a powerful technique to study molecules which are not amenable to either solution NMR or X-ray crystallography. Despite the reporting of individual atomic resolution structures of membrane proteins and amyloid fibrils based on solid-state NMR data, the application is far from routine. One major obstacle that hinders structure determination by solid-state NMR is the overall lower quality of the solid-state NMR spectra. It is therefore necessary to increase the robustness of the computer algorithms in order to improve the results when using lower quality solid-state NMR spectra. The chapter “Structure calculations of the model protein GB1 from solid-state NMR data” presents structure calculations on the basis of a set of two-dimensional solid-state NMR experiments of the model protein GB1. The most important result obtained from these test calculations is that the limitation of structural accuracy can be attributed to inaccurate distance information resulting from the limited correlation between peak intensities and distance, which is especially severe in spin diffusion-based solid-state NMR experiments. The chapter “Full relaxation matrix-based correction of relayed polarization transfer for solid-state NMR structure calculation” therefore introduces a method which corrects experimental peak intensities for spin diffusion in order to improve the distance information from solid-state NMR spectra. The results show that the structural accuracy can be significantly improved when using the corrected distance information, however, strongly dependent on the preliminary structural model that is required as input for the method.