Ensemble Of NMR-derived Structures Research Articles

ProCS15: a DFT-based chemical shift predictor for backbone and Cβ atoms in proteins.

We present ProCS15: a program that computes the isotropic chemical shielding values of backbone and Cβ atoms given a protein structure in less than a second. ProCS15 is based on around 2.35 million OPBE/6-31G(d,p)//PM6 calculations on tripeptides and small structural models of hydrogen-bonding. The ProCS15-predicted chemical shielding values are compared to experimentally measured chemical shifts for Ubiquitin and the third IgG-binding domain of Protein G through linear regression and yield RMSD values of up to 2.2, 0.7, and 4.8 ppm for carbon, hydrogen, and nitrogen atoms. These RMSD values are very similar to corresponding RMSD values computed using OPBE/6-31G(d,p) for the entire structure for each proteins. These maximum RMSD values can be reduced by using NMR-derived structural ensembles of Ubiquitin. For example, for the largest ensemble the largest RMSD values are 1.7, 0.5, and 3.5 ppm for carbon, hydrogen, and nitrogen. The corresponding RMSD values predicted by several empirical chemical shift predictors range between 0.7–1.1, 0.2–0.4, and 1.8–2.8 ppm for carbon, hydrogen, and nitrogen atoms, respectively.

CoNSEnsX: an ensemble view of protein structures and NMR-derived experimental data

BackgroundIn conjunction with the recognition of the functional role of internal dynamics of proteins at various timescales, there is an emerging use of dynamic structural ensembles instead of individual conformers. These ensembles are usually substantially more diverse than conventional NMR ensembles and eliminate the expectation that a single conformer should fulfill all NMR parameters originating from 1016 - 1017 molecules in the sample tube. Thus, the accuracy of dynamic conformational ensembles should be evaluated differently to that of single conformers.ResultsWe constructed the web application CoNSEnsX (Consistency of NMR-derived Structural Ensembles with eXperimental data) allowing fast, simple and convenient assessment of the correspondence of the ensemble as a whole with diverse independent NMR parameters available. We have chosen different ensembles of three proteins, human ubiquitin, a small protease inhibitor and a disordered subunit of cGMP phosphodiesterase 5/6 for detailed evaluation and demonstration of the capabilities of the CoNSEnsX approach.ConclusionsOur results present a new conceptual method for the evaluation of dynamic conformational ensembles resulting from NMR structure determination. The designed CoNSEnsX approach gives a complete evaluation of these ensembles and is freely available as a web service at http://consensx.chem.elte.hu.

Conformation and dynamics of the three-helix bundle UBA domain of p62 from experiment and simulation

The ubiquitin associated domain of p62 is a small three-helix bundle of approximately 50 residues that mediates the recognition of polyubiquitin chains and ubiquitylated substrates. The solution structure of a 52 residue construct containing this domain has been characterized using heteronuclear nuclear magnetic resonance (NMR) methods. The resulting ensemble of NMR-derived structures was used in molecular dynamics (MD) simulations to investigate the equilibrium conformation and dynamics of this domain. NOE and (15)N relaxation data have been used to validate the structural ensemble produced by the MD simulations and show a good correlation for residues in regions of secondary structure. A similar approach was taken using an ensemble of structures from the MD simulations to calculate electronic circular dichroism (CD) and IR spectra from first principles with an encouraging correlation with the experimental CD and IR data.

Clustering algorithms for identifying core atom sets and for assessing the precision of protein structure ensembles

An important open question in the field of NMR-based biomolecular structure determination is how best to characterize the precision of the resulting ensemble of structures. Typically, the RMSD, as minimized in superimposing the ensemble of structures, is the preferred measure of precision. However, the presence of poorly determined atomic coordinates and multiple "RMSD-stable domains"--locally well-defined regions that are not aligned in global superimpositions--complicate RMSD calculations. In this paper, we present a method, based on a novel, structurally defined order parameter, for identifying a set of core atoms to use in determining superimpositions for RMSD calculations. In addition we present a method for deciding whether to partition that core atom set into "RMSD-stable domains" and, if so, how to determine partitioning of the core atom set. We demonstrate our algorithm and its application in calculating statistically sound RMSD values by applying it to a set of NMR-derived structural ensembles, superimposing each RMSD-stable domain (or the entire core atom set, where appropriate) found in each protein structure under consideration. A parameter calculated by our algorithm using a novel, kurtosis-based criterion, the epsilon-value, is a measure of precision of the superimposition that complements the RMSD. In addition, we compare our algorithm with previously described algorithms for determining core atom sets. The methods presented in this paper for biomolecular structure superimposition are quite general, and have application in many areas of structural bioinformatics and structural biology.

Solution Structure of the Pro-hormone Convertase 1 Pro-domain from Mus musculus

The solution structure of the mouse pro-hormone convertase (PC) 1 pro-domain was determined using heteronuclear NMR spectroscopy and is the first structure to be obtained for any of the domains in the convertase family. The ensemble of NMR-derived structures shows a well-ordered core consisting of a four-stranded antiparallel β-sheet with two α-helices packed against one side of this sheet. Sequence homology suggests that the other eukaryotic PC pro-domains will have the same overall fold and most of the residues forming the hydrophobic core of PC1 are highly conserved within the PC family. However, some of the core residues are predicted by homology to be replaced by polar amino acid residues in other PC pro-domains and this may help to explain their marginal stability. Interestingly, the folding topology observed here is also seen for the pro-domain of bacterial subtilisin despite little or no sequence homology. Both the prokaryotic and eukaryotic structures have hydrophobic residues clustered on the solvent-accessible surface of their β-sheets although the individual residue types differ. In the bacterial case this region is buried at the binding interface with the catalytic domain and, in the eukaryotic PC family, these surface residues are conserved. We therefore propose that the hydrophobic patch in the PC1 pro-domain is involved in the binding interface with its cognate catalytic domain in a similar manner to that seen for the bacterial system. The PC1 pro-domain structure also reveals potential mechanisms for the acid-induced dissociation of the complex between pro- and catalytic domains.

An automated approach for clustering an ensemble of NMR-derived protein structures into conformationally related subfamilies.

Unlike structures determined by X-ray crystallography, which are deposited in the Brookhaven Protein Data Bank (Abola et al., 1987) as a single structure, each NMR-derived structure is often deposited as an ensemble containing many structures, each consistent with the restraint set used. However, there is often a need to select a single 'representative' structure, or a 'representative' subset of structures, from such an ensemble. This is useful, for example, in the case of homology modelling or when compiling a relational database of protein structures. It has been shown that cluster analysis, based on overall fold, followed by selection of the structure closest to the centroid of the largest cluster, is likely to identify a structure more representative of the ensemble than the commonly used minimized average structure (Sutcliffe, 1993). Two approaches to the problem of clustering ensembles of NMR-derived structures have been described. One of these (Adzhubei et al., 1995) performs the pairwise superposition of all structures using C a atoms to generate a set of r.m.s. distances. After cluster analysis based on these distances, a user-defined cut-off is required to determine the final membership of clusters and therefore the representative structures. The other approach (Diamond, 1995) uses collective superpositions and rigid-body transformations. Again, the position at which to draw a cut-off based on the particular clustering pattern was not addressed. Whenever fixed values are used for the cut-off in clustering, there is a danger of missing 'true' clusters under the threshold imposed by the rigid cut-off value. Considering the highly diverse nature of NMR-derived ensembles of proteins, it would seem most appropriate to avoid the use of predefined values for determining clusters. In fact, of the 302 ensembles we have studied, the average pairwise r.m.s. distance across an ensemble varied from 0.29 to 11.3 A (mean value 3.0, SD 1.9 A). Here we present an automated method for cut-off determination that avoids the dangers of using fixed values for this purpose. We have developed a computer program that automatically, systematically and rapidly (i) clusters an ensemble of structures into a set of conformationally related subfamilies, and (ii) selects a representative structure from each cluster. The program uses the method of average linkage to define how clusters are built up, followed by the application of a penalty function that seeks to minimize simultaneously the number of clusters