Computer assisted assignment of ¹³C or ¹⁵N edited 3D-NOESY-HSQC spectra using back calculated and experimental spectra

A new tool, for the simulation of 15N or 13C edited 3D-NOESY-HSQC spectra using the complete relaxation matrix approach, has been developed and integrated in the program AURELIA. This tool should be particularly useful for the fast and reliable computer assisted assignment of 3D-NOESY-HSQC spectra by comparing back-calculated and experimental spectra in an iterative process. Folded spectra are sometimes used to enhance the digital resolution in the indirect dimensions of multidimensional spectra. However, these spectra are usually difficult to analyze. To simplify this assignment process we have incorporated the simulation and automated annotation of folded peaks into the program. It is hereby possible to simulate multiple folding in all three dimensions of 3D 15N- or 13C-NOESY-HSQC spectra. By comparing experimental 3D-NOESY-HSQC spectra with spectra back calculated from a single trial structure or a set of trial structures, a user can easily check if the final structures explain all experimental NOEs. The new feature has been successfully tested with the histidine-containing phosphocarrier protein HPr from Staphylococcus carnosus

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

Determination of mean and standard deviation of dihedral angles

Backbone torsional angles are a characteristic and useful parameter for the description and characterisation of protein structures determined by x-ray crystallography or NMR spectroscopy. For the comparison of an ensemble of three-dimensional structures the calculation of the statistical parameters mean and standard deviation would be very useful. However, they are not defined unambiguously for periodic quantities such as the dihedral angles. In this paper a plausible and unique definition of these parameters is introduced and a straightforward method for their calculation is given

Use of global symmetries in automated signal class recognition by a bayesian method

Automated or semiautomated pattern recognition in multidimensional NMR spectroscopy is strongly hampered by the large number of noise and artifact peaks occurring under practical conditions. A general Bayesian method which is able to assign probabilities that observed peaks are members of given signal classes (e.g., the class of true resonance peaks or the class of noise and artifact peaks) was proposed previously. The discriminative power of this approach is dependent on the choice of the properties characterizing the peaks. The automated class recognition is improved by the addition of a nonlocal feature, the similarities of peak shapes in symmetry-related positions. It turns out that this additional property strongly decreases the overlap of the multivariate probability distributions for true signals and noise and hence largely increases the discrimination of true resonance peaks from noise and artifacts. Copyright 1997 Academic Press. Copyright 1997Academic Pres

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

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

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

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