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.

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.

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.

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 Press

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.

AURELIA, a program for computer-aided analysis of multidimensional NMR spectra

AURELIA is an advanced program for the computer-aided evaluation of two-, three- and four-dimensional NMR spectra of any type of molecule. It can be used for the analysis of spectra of small molecules as well as for evaluation of complicated spectra of biological macromolecules such as proteins. AURELIA is highly interactive and offers a large number of tools, such as artefact reduction, cluster and multiplet analysis, spin system searches, resonance assignments, automated calculation of volumes in multidimensional spectra, calculation of distances with different approaches, including the full relaxation matrix approach, Bayesian analysis of peak features, correlation of molecular structures with NMR data, comparison of spectra via spectral algebra and pattern match techniques, automated sequential assignments on the basis of triple resonance spectra, and automatic strip calculation. In contrast to most other programs, many tasks are performed automatically.

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

A 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 Bayes's 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.

Two-dimensional 1H NMR studies on HPr protein from Staphylococcus aureus: complete sequential assignments and secondary structure

Complete sequence-specific assignments of the 1H NMR spectrum of HPr protein from Staphylococcus aureus were obtained by two-dimensional NMR methods. Important secondary structure elements that can be derived from the observed nuclear Overhauser effects are a large antiparallel beta-pleated sheet consisting of four strands, A, B, C, D, a segment SAB consisting of an extended region around the active-center histidine (His-15) and an alpha-helix, a half-turn between strands B and C, a segment SCD which shows no typical secondary structure, and the alpha-helical, C-terminal segment S(term). These general structural features are similar to those found earlier in HPr proteins from different microorganisms such as Escherichia coli, Bacillus subtilis, and Streptococcus faecalis.

C-terminal structure and mobility of rabbit skeletal muscle light meromyosin as studied by one- and two-dimensional 1H NMR spectroscopy and X-ray small-angle scattering

Intact rabbit myosin and two different C-terminal fragments of rabbit muscle light meromyosin (LMM) expressed in Escherichia coli, LMM-30, and LMM-30C', were studied by 1H NMR spectroscopy. X-ray small-angle scattering shows that at high ionic strength two polypeptide chains of LMM-30 (which consists of the C-terminal 262 amino acids of myosin heavy chain) or LMM-30C' (which corresponds to LMM-30 but lacks the last 17 residues) assemble to form an alpha-helical coiled-coil as it is found also in myosin. The last 12 C-terminal residues of one polypeptide chain of LMM-30 and the last 9 C-terminal residues of the other chain are very mobile. The last 8 residues of the two strands are equivalent from the NMR point of view and unfolded; the valine residues in position 255 in the two strands are not equivalent, suggesting an interaction between the two strands, Ser-252, Arg-253, and Asp-254 are completely immobilized in one of the polypeptide strands and partly mobile in the other. Essentially the same pattern is observed in intact myosin. In spite of the large molecular weights of LMM-30 and LMM-30C', it is possible to resolve almost all aromatic residues and to determine the pK values of all the 4 tyrosine and of 9 (out of 10) histidine residues. The tyrosine residues in the two strands are equivalent in the two polypeptide chains and both have a pK of 10.5. The pK values of the histidine residues vary between 5.7 and 7.0.

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.