Two-dimensional NMR spectroscopy with temperature-sweep.

Two-dimensional nuclear magnetic resonance (NMR) spectroscopy is useful for studying temperature-dependent effects on molecular structure. However, experimental time is usually long, because sampling is repeated at several temperatures. A novel solution to the problem is proposed, in which signal sampling is performed in parallel to the linear temperature-sweep.

Singular spectrum analysis for an automated solvent artifact removal and baseline correction of 1D NMR spectra.

NMR spectroscopy in biology and medicine is generally performed in aqueous solutions, thus in (1)H NMR spectroscopy, the dominant signal often stems from the partly suppressed solvent and can be many orders of magnitude larger than the resonances of interest. Strong solvent signals lead to a disappearance of weak resonances of interest close to the solvent artifact and to base plane variations all over the spectrum. The AUREMOL-SSA/ALS approach for automated solvent artifact removal and baseline correction has been originally developed for multi-dimensional NMR spectroscopy. Here, we describe the necessary adaptations for an automated application to one-dimensional NMR spectra. Its core algorithm is still based on singular spectrum analysis (SSA) applied on time domain signals (FIDs) and it is still combined with an automated baseline correction (ALS) in the frequency domain. However, both steps (SSA and ALS) have been modified in order to achieve optimal results when dealing with one-dimensional spectra. The performance of the method has been tested on one-dimensional synthetic and experimental spectra including the back-calculated spectrum of HPr protein and an experimental spectrum of a human urine sample. The latter has been recorded with the typically used NOESY-type 1D pulse sequence including water pre-saturation. Furthermore, the fully automated AUREMOL-SSA/ALS procedure includes the managing of oversampled, digitally filtered and zero-filled data and the correction of the frequency domain phase shift caused by the group delay time shift from the digital finite response filtering.

Automated solvent artifact removal and base plane correction of multidimensional NMR protein spectra by AUREMOL-SSA.

Strong solvent signals lead to a disappearance of weak protein signals close to the solvent resonance frequency and to base plane variations all over the spectrum. AUREMOL-SSA provides an automated approach for solvent artifact removal from multidimensional NMR protein spectra. Its core algorithm is based on singular spectrum analysis (SSA) in the time domain and is combined with an automated base plane correction in the frequency domain. The performance of the method has been tested on synthetic and experimental spectra including two-dimensional NOESY and TOCSY spectra and a three-dimensional (1)H,(13)C-HCCH-TOCSY spectrum. It can also be applied to frequency domain spectra since an optional inverse Fourier transformation is included in the algorithm.

Chemical shift optimization in multidimensional NMR spectra by AUREMOL-SHIFTOPT.

A problem often encountered in multidimensional NMR-spectroscopy is that an existing chemical shift list of a protein has to be used to assign an experimental spectrum but does not fit sufficiently well for a safe assignment. A similar problem occurs when temperature or pressure series of n-dimensional spectra are to be evaluated automatically. We have developed two different algorithms, AUREMOL-SHIFTOPT1 and AUREMOL-SHIFTOPT2 that fulfill this task. In the present contribution their performance is analyzed employing a set of simulated and experimental two-dimensional and three-dimensional spectra obtained from three different proteins. A new z-score based on atom and amino acid specific chemical shift distributions is introduced to weight the chemical shift contributions in different dimensions properly.

Combined chemical shift changes and amino acid specific chemical shift mapping of protein-protein interactions.

Protein-protein interactions are often studied by chemical shift mapping using solution NMR spectroscopy. When heteronuclear data are available the interaction interface is usually predicted by combining the chemical shift changes of different nuclei to a single quantity, the combined chemical shift perturbation Deltadelta comb In this paper different procedures (published and non-published) to calculate Deltadelta comb are examined that include a variety of different functional forms and weighting factors for each nucleus. The predictive power of all shift mapping methods depends on the magnitude of the overlap of the chemical shift distributions of interacting and non-interacting residues and the cut-off criterion used. In general, the quality of the prediction on the basis of chemical shift changes alone is rather unsatisfactory but the combination of chemical shift changes on the basis of the Hamming or the Euclidian distance can improve the result. The corrected standard deviation to zero of the combined chemical shift changes can provide a reasonable cut-off criterion. As we show combined chemical shifts can also be applied for a more reliable quantitative evaluation of titration data.

AUREMOL-RFAC-3D, combination of R-factors and their use for automated quality assessment of protein solution structures.

We present here the computer program AUREMOL-RFAC-3D that is a generalization of the previously published program RFAC for the fully automated estimation of residual indices (R-factors) from 2D NOESY spectra. It is part of the larger AUREMOL software package (www.auremol.de). RFAC-3D calculates R-factors directly from two-dimensional homonuclear NOESY spectra as well as from three-dimensional (15)N or (13)C edited NOESY-HSQC spectra and thus extends the application range to larger proteins. The fully automated method includes automated peak picking and integration, a Bayesian noise and artifact recognition and the use of the complete relaxation matrix formalism. To enhance the reliability of the calculated R-factors the method is also generalized to calculate combined R-factors from a set of 2D and 3D-spectra. For an optimal combination of the information derived from different sources a plausible formalism had to be derived. In addition, we present a novel direct R-factors based measure that correlates an R-factors as defined in this paper to the root mean square deviation of the actual structure from the optimal structure. The new program has been successfully tested on the histidine containing phosphocarrier protein (HPr) from Staphylococcus carnosus and on the Ras-binding domain (RBD) of the Ral guanine-nucleotide dissociation stimulation factor (RalGDS).

A general method for the unbiased improvement of solution NMR structures by the use of related X-ray data, the AUREMOL-ISIC algorithm.

BACKGROUND: Rapid and accurate three-dimensional structure determination of biological macromolecules is mandatory to keep up with the vast progress made in the identification of primary sequence information. During the last few years the amount of data deposited in the protein data bank has substantially increased providing additional information for novel structure determination projects. The key question is how to combine the available database information with the experimental data of the current project ensuring that only relevant information is used and a correct structural bias is produced. For this purpose a novel fully automated algorithm based on Bayesian reasoning has been developed. It allows the combination of structural information from different sources in a consistent way to obtain high quality structures with a limited set of experimental data. The new ISIC (Intelligent Structural Information Combination) algorithm is part of the larger AUREMOL software package. RESULTS: Our new approach was successfully tested on the improvement of the solution NMR structures of the Ras-binding domain of Byr2 from Schizosaccharomyces pombe, the Ras-binding domain of RalGDS from human calculated from a limited set of NMR data, and the immunoglobulin binding domain from protein G from Streptococcus by their corresponding X-ray structures. In all test cases clearly improved structures were obtained. The largest danger in using data from other sources is a possible bias towards the added structure. In the worst case instead of a refined target structure the structure from the additional source is essentially reproduced. We could clearly show that the ISIC algorithm treats these difficulties properly. CONCLUSION: In summary, we present a novel fully automated method to combine strongly coupled knowledge from different sources. The combination with validation tools such as the calculation of NMR R-factors strengthens the impact of the method considerably since the improvement of the structures can be assessed quantitatively. The ISIC method can be applied to a large number of similar problems where the quality of the obtained three-dimensional structures is limited by the available experimental data like the improvement of large NMR structures calculated from sparse experimental data or the refinement of low resolution X-ray structures. Also structures may be refined using other available structural information such as homology models.

Improved simulation of NOESY spectra by RELAX-JT2 including effects of J-coupling, transverse relaxation and chemical shift anisotrophy.

RELAX-JT2 is an extension of RELAX, a program for the simulation of 1H 2D NOESY spectra and (15)N or (13)C edited 3D NOESY-HSQC spectra of biological macromolecules. In addition to the already existing NOE-simulation it allows the proper simulation of line shapes by the integrated calculation of T(2) times and multiplet structures caused by J-couplings. Additionally the effects of relaxation mediated by chemical shift anisotropy are taken into account. The new routines have been implemented in the program AUREMOL, which aims at the automated NMR structure determination of proteins in solution. For a manual or automatic assignment of experimental spectra that is based on the comparison with the corresponding simulated spectra, the additional line shape information now available is a valuable aid. The new features have been successfully tested with the histidine-containing phosphocarrier protein HPr from Staphylococcus carnosus.

Automated assignment of NOESY NMR spectra using a knowledge based method (KNOWNOE).

Automated assignment of NOESY spectra is a prerequisite for automated structure determination of biological macromolecules. With the program KNOWNOE we present a novel, knowledge based approach to this problem. KNOWNOE is devised to work directly with the experimental spectra without interference of an expert. Besides making use of routines already implemented in AUREMOL, it contains as a central part a knowledge driven Bayesian algorithm for solving ambiguities in the NOE assignments. These ambiguities mainly arise from chemical shift degeneration which allows multiple assignments of cross peaks. Using a set of 326 protein NMR structures, statistical tables in the form of atom-pairwise volume probability distributions (VPDs) were derived. VPDs for all assignment possibilities relevant to the assignments of interproton NOEs were calculated. With these data for a given cross peak with N possible assignments Ai (i = 1,...,N) the conditional probabilities P(Ai, a/V0) can be calculated that the assignment Ai determines essentially all (a-times) of the cross peak volume V0. An assignment Ak with a probability P(Ak, a/V0) higher than 0.8 is transiently considered as unambiguously assigned. With a list of unambiguously assigned peaks a set of structures is calculated. These structures are used as input for a next cycle of iteration where a distance threshold Dmax is dynamically reduced. The program KNOWNOE was tested on NOESY spectra of a medium size protein, the cold shock protein (TmCsp) from Thermotoga maritima. The results show that a high quality structure of this protein can be obtained by automated assignment of NOESY spectra which is at least as good as the structure obtained from manual data evaluation.