Effects of NMR spectral resolution on protein structure calculation

Adequate digital resolution and signal sensitivity are two critical factors for protein structure determinations by solution NMR spectroscopy. The prime objective for obtaining high digital resolution is to resolve peak overlap, especially in NOESY spectra with thousands of signals where the signal analysis needs to be performed on a large scale. Achieving maximum digital resolution is usually limited by the practically available measurement time. We developed a method utilizing non-uniform sampling for balancing digital resolution and signal sensitivity, and performed a large-scale analysis of the effect of the digital resolution on the accuracy of the resulting protein structures. Structure calculations were performed as a function of digital resolution for about 400 proteins with molecular sizes ranging between 5 and 33 kDa. The structural accuracy was assessed by atomic coordinate RMSD values from the reference structures of the proteins. In addition, we monitored also the number of assigned NOESY cross peaks, the average signal sensitivity, and the chemical shift spectral overlap. We show that high resolution is equally important for proteins of every molecular size. The chemical shift spectral overlap depends strongly on the corresponding spectral digital resolution. Thus, knowing the extent of overlap can be a predictor of the resulting structural accuracy. Our results show that for every molecular size a minimal digital resolution, corresponding to the natural linewidth, needs to be achieved for obtaining the highest accuracy possible for the given protein size using state-of-the-art automated NOESY assignment and structure calculation methods

Quantification of H/D Isotope Effects on Protein Hydrogen-bonds by h3JNC′ and 1JNC′ Couplings and Peptide Group 15N and 13C′ Chemical Shifts

The effect of hydrogen/deuterium exchange on proteinhydrogen bond coupling constants h3JNC′ has been investigated in the small globular protein ubiquitin. The couplings across deuterated or protonated hydrogen bonds were measured by a long-range quantitative HA(CACO)NCO experiment. The analysis is combined with a determination of the HN/DN isotope effect on the amide group 1JNC′ couplings and the 15N and 13C′ chemical shifts. On average, H-bond deuteration exchange weakens h3JNC′ and strengthens 1JNC′ couplings. A correlation is found between the size of the 15N isotope shift, the 15N chemical shift, and the h3JNC′ coupling constants. The data are consistent with a reduction of donor-acceptor overlap as expected from the classical Ubbelohde effect and the common understanding that HN/DN exchange leads to a shortening of the N-hydron bond length. Abbreviations: H-bond - hydrogen bon

Automatic assignment of protein backbone resonances by direct spectrum inspection in targeted acquisition of NMR data

The necessity to acquire large multidimensional datasets, a basis for assignment of NMR resonances, results in long data acquisition times during which substantial degradation of a protein sample might occur. Here we propose a method applicable for such a protein for automatic assignment of backbone resonances by direct inspection of multidimensional NMR spectra. In order to establish an optimal balance between completeness of resonance assignment and losses of cross-peaks due to dynamic processes/degradation of protein, assignment of backbone resonances is set as a stirring criterion for dynamically controlled targeted nonlinear NMR data acquisition. The result is demonstrated with the 12kDa 13C,15N-labeled apo-form of heme chaperone protein CcmE, where hydrolytic cleavage of 29 C-terminal amino acids is detected. For this protein, 90 and 98% of manually assignable resonances are automatically assigned within 10 and 40h of nonlinear sampling of five 3D NMR spectra, respectively, instead of 600h needed to complete the full time domain grid. In addition, resonances stemming from degradation products are identified. This study indicates that automatic resonance assignment might serve as a guiding criterion for optimal run-time allocation of NMR resources in applications to proteins prone to degradatio

Expitope 2.0: a tool to assess immunotherapeutic antigens for their potential cross-reactivity against naturally expressed proteins in human tissues

Abstract Background Adoptive immunotherapy offers great potential for treating many types of cancer but its clinical application is hampered by cross-reactive T cell responses in healthy human tissues, representing serious safety risks for patients. We previously developed a computational tool called Expitope for assessing cross-reactivity (CR) of antigens based on tissue-specific gene expression. However, transcript abundance only indirectly indicates protein expression. The recent availability of proteome-wide human protein abundance information now facilitates a more direct approach for CR prediction. Here we present a new version 2.0 of Expitope, which computes all naturally possible epitopes of a peptide sequence and the corresponding CR indices using both protein and transcript abundance levels weighted by a proposed hierarchy of importance of various human tissues. Results We tested the tool in two case studies: The first study quantitatively assessed the potential CR of the epitopes used for cancer immunotherapy. The second study evaluated HLA-A*02:01-restricted epitopes obtained from the Immune Epitope Database for different disease groups and demonstrated for the first time that there is a high variation in the background CR depending on the disease state of the host: compared to a healthy individual the CR index is on average two-fold higher for the autoimmune state, and five-fold higher for the cancer state. Conclusions The ability to predict potential side effects in normal tissues helps in the development and selection of safer antigens, enabling more successful immunotherapy of cancer and other diseases

Automatic assignment of protein backbone resonances by direct spectrum inspection in targeted acquisition of NMR data

ISSN:0925-2738ISSN:1573-500

Peak picking NMR spectral data using non-negative matrix factorization

Background: Simple peak-picking algorithms, such as those based on lineshape fitting, perform well when peaks are completely resolved in multidimensional NMR spectra, but often produce wrong intensities and frequencies for overlapping peak clusters. For example, NOESY-type spectra have considerable overlaps leading to significant peak-picking intensity errors, which can result in erroneous structural restraints. Precise frequencies are critical for unambiguous resonance assignments. Results: To alleviate this problem, a more sophisticated peaks decomposition algorithm, based on non-negative matrix factorization (NMF), was developed. We produce peak shapes from Fourier-transformed NMR spectra. Apart from its main goal of deriving components from spectra and producing peak lists automatically, the NMF approach can also be applied if the positions of some peaks are known a priori, e.g. from consistently referenced spectral dimensions of other experiments. Conclusions: Application of the NMF algorithm to a three-dimensional peak list of the 23 kDa bi-domain section of the RcsD protein (RcsD-ABL-HPt, residues 688-890) as well as to synthetic HSQC data shows that peaks can be picked accurately also in spectral regions with strong overlap

Chemical shift spectral overlap (CSSO) indices as a function of the digital resolution.

<p>(A) ¹³C-resolved NOESY peak lists. The fine dotted line shows the average CSSO index for 149 protein structures with heavy atom RMSD to the reference structure below 1.5 Å calculated at the highest digital resolution (obtained by sampling 1250 points). Dashed, dotted, and solid lines correspond to 210, 14, and 4 protein structures with RMSD values of 1.5–2.5, 2.5–4.0, and more than 4.0 Å, respectively. (B) Same data for ¹⁵N-resolved NOESY peak lists.</p