Klaus-Peter Neidig

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 Aidetermines essentially all (a-times) of the cross peak volume V0. An assignment Akwith 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 Dmaxis 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.

Computer aided evaluation of two-dimensional NMR spectra of proteins

A computer program for the automatic evaluation of two-dimensional NMR spectra of peptides and proteins has been developed. The used strategy is described, the advantages and limits of this approach are discussed. The program was successfully tested on a COSY-spectrum of the neuropeptide Glp-Pro-Pro-Gly-Gly-Ser-Lys-Val-Ile-Leu-Phe from hydra, resulting in a drastic reduction of the time needed for the evaluation of two-dimensional NMR data.

RFAC, a program for automated NMR R-factor estimation.

A computer program (RFAC) has been developed, which allows the automated estimation of residual indices (R-factors) for protein NMR structures and gives a reliable measure for the quality of the structures. The R-factor calculation is based on the comparison of experimental and simulated 1H NOESY NMR spectra. The approach comprises an automatic peak picking and a Bayesian analysis of the data, followed by an automated structure based assignment of the NOESY spectra and the calculation of the R-factor. The major difference to previously published R-factor definitions is that we take the non-assigned experimental peaks into account as well. The number and the intensities of the non-assigned signals are an important measure for the quality of an NMR structure. It turns out that for different problems optimally adapted R-factors should be used which are defined in the paper. The program allows to compute a global R-factor, different R-factors for the intra residual NOEs, the inter residual NOEs, sequential NOEs, medium range NOEs and long range NOEs. Furthermore, R-factors can be calculated for various user defined parts of the molecule or it is possible to obtain a residue-by-residue R-factor. Another possibility is to sort the R-factors according to their corresponding distances. The summary of all these different R-factors should allow the user to judge the structure in detail. The new program has been successfully tested on two medium sized proteins, the cold shock protein (TmCsp) from Termotoga maritima and the histidine containing protein (HPr) from Staphylococcus carnosus. A comparison with a previously published R-factor definition shows that our approach is more sensitive to errors in the calculated structure.

A general Bayesian method for an automated signal class recognition in 2D NMR spectra combined with a multivariate discriminant analysis

SummaryA generally applicable method for the automated classification of 2D NMR peaks has been developed, based on a Bayesian approach coupled to a multivariate linear discriminant analysis of the data. The method can separate true NMR signals from noise signals, solvent stripes and artefact signals. The analysis relies on the assumption that the different signal classes have different distributions of specific properties such as line shapes, line widths and intensities. As to be expected, the correlation network of the distributions of the selected properties affects the choice of the discriminant function and the final selection of signal properties. The classification rule for the signal classes was deduced from Bayess theorem. The method was successfully tested on a NOESY spectrum of HPr protein from Staphylococcus aureus. The calculated probabilities for the different signal class memberships are realistic and reliable, with a high efficiency of discrimination between peaks that are true NOE signals and those that are not.

Improved representation of two-dimensional NMR spectra by local rescaling

Two-dimensional NMR spectroscopy has developed into a powerful method for the determination of the three-dimensional structure of biological macromolecules ( 1-3). The information used for the calculation of the spatial structure must be extracted from the rather complex 2D NMR spectra of macromolecules which usually consist of a very large number of resonance peaks. Although automated or computeraided evaluation of 2D NMR spectra is becoming more and more important (414)) 2D experiments are still evaluated by inspection of the contour plot, a graphical representation of the two-dimensional spectra ( 15). The information content of a contour plot depends on the plot levels chosen: too low plot levels obscure cross peaks by noise and by artifacts; too high plot levels make weak but significant cross peaks

Tools for the automated assignment of high-resolution three-dimensional protein NMR spectra based on pattern recognition techniques

One of the major bottlenecks in the determination of proteinstructures by NMR is in the evaluation of the data produced by theexperiments. An important step in this process is assignment, where thepeaks in the spectra are assigned to specific spins within specificresidues. In this paper, we discuss a spin system assignment tool based onpattern recognition techniques. This tool employs user-specified ’templates‘to search for patterns of peaks in the original spectra; these patterns maycorrespond to side-chain or backbone fragments. Multiple spectra willnormally be searched simultaneously to reduce the impact of noise. Thesearch generates a preliminary list of putative assignments, which arefiltered by a set of heuristic algorithms to produce the final results list.Each result contains a set of chemical shift values plus information aboutthe peaks found. The results may be used as input for combinatorialroutines, such as sequential assignment procedures, in place of peak lists.Two examples are presented, in which (i) HCCH-COSY and -TOCSY spectra arescanned for side-chain spin systems; and (ii) backbone spin systems aredetected in a set of spectra comprising HNCA, HN(CO)CA, HNCO, HN(CA)CO,CBCANH and CBCA(CO)NH.

Cluster analysis and multiplet pattern recognition in two-dimensional NMR spectra

Abstract A general and efficient strategy for the recognition of arbitrary multiplet patterns in two-dimensional nuclear magnetic resonance spectra has been developed. It comprises cluster analysis, feature extraction, and pattern matching techniques. The corresponding C routines embedded in the graphical environment of the program AURELIA were tested successfully on two-dimensional nuclear magnetic resonance spectra of the neuropeptide head activator and the HPr proteins of Staphylococcus aureus and Streptococcus faecalis .

NMR in the SPINE Structural Proteomics project

This paper describes the developments, role and contributions of the NMR spectroscopy groups in the Structural Proteomics In Europe (SPINE) consortium. Focusing on the development of high-throughput (HTP) pipelines for NMR structure determinations of proteins, all aspects from sample preparation, data acquisition, data processing, data analysis to structure determination have been improved with respect to sensitivity, automation, speed, robustness and validation. Specific highlights are protonless (13)C-direct detection methods and inferential structure determinations (ISD). In addition to technological improvements, these methods have been applied to deliver over 60 NMR structures of proteins, among which are five that failed to crystallize. The inclusion of NMR spectroscopy in structural proteomics pipelines improves the success rate for protein structure determinations.

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

BackgroundRapid 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 (I ntelligent S tructural I nformation C ombination) algorithm is part of the larger AUREMOL software package.ResultsOur 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.ConclusionIn 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.

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.