Validation of archived chemical shifts through atomic coordinates.

The public archives containing protein information in the form of NMR chemical shift data at the BioMagResBank (BMRB) and of 3D structure coordinates at the Protein Data Bank are continuously expanding. The quality of the data contained in these archives, however, varies. The main issue for chemical shift values is that they are determined relative to a reference frequency. When this reference frequency is set incorrectly, all related chemical shift values are systematically offset. Such wrongly referenced chemical shift values, as well as other problems such as chemical shift values that are assigned to the wrong atom, are not easily distinguished from correct values and effectively reduce the usefulness of the archive. We describe a new method to correct and validate protein chemical shift values in relation to their 3D structure coordinates. This method classifies atoms using two parameters: the per-atom solvent accessible surface area (as calculated from the coordinates) and the secondary structure of the parent amino acid. Through the use of Gaussian statistics based on a large database of 3220 BMRB entries, we obtain per-entry chemical shift corrections as well as Z scores for the individual chemical shift values. In addition, information on the error of the correction value itself is available, and the method can retain only dependable correction values. We provide an online resource with chemical shift, atom exposure, and secondary structure information for all relevant BMRB entries (http://www.ebi.ac.uk/pdbe/nmr/vasco) and hope this data will aid the development of new chemical shift-based methods in NMR.

Relationship between chemical shift value and accessible surface area for all amino acid atoms.

BACKGROUND: Chemical shifts obtained from NMR experiments are an important tool in determining secondary, even tertiary, protein structure. The main repository for chemical shift data is the BioMagResBank, which provides NMR-STAR files with this type of information. However, it is not trivial to link this information to available coordinate data from the PDB for non-backbone atoms due to atom and chain naming differences, as well as sequence numbering changes. RESULTS: We here describe the analysis of a consistent set of chemical shift and coordinate data, in which we focus on the relationship between the per-atom solvent accessible surface area (ASA) in the reported coordinates and their reported chemical shift value. The data is available online on http://www.ebi.ac.uk/pdbe/docs/NMR/shiftAnalysis/index.html. CONCLUSION: Atoms with zero per-atom ASA have a significantly larger chemical shift dispersion and often have a different chemical shift distribution compared to those that are solvent accessible. With higher per-atom ASA, the chemical shift values also tend towards random coil values. The per-atom ASA, although not the determinant of the chemical shift, thus provides a way to directly correlate chemical shift information to the atomic coordinates.

Influence of different assignment conditions on the determination of symmetric homodimeric structures with ARIA.

The ambiguous restraint for iterative assignment (ARIA) approach for NMR structure calculation is evaluated for symmetric homodimeric proteins by assessing the effect of several data analysis and assignment methods on the structure quality. In particular, we study the effects of network anchoring and spin-diffusion correction. The spin-diffusion correction improves the protein structure quality systematically, whereas network anchoring enhances the assignment efficiency by speeding up the convergence and coping with highly ambiguous data. For some homodimeric folds, network anchoring has been proved essential for unraveling both chain and proton assignment ambiguities.

Accurate NMR structures through minimization of an extended hybrid energy.

The use of generous distance bounds has been the hallmark of NMR structure determination. However, bounds necessitate the estimation of data quality before the calculation, reduce the information content, introduce human bias, and allow for major errors in the structures. Here, we propose a new rapid structure calculation scheme based on Bayesian analysis. The minimization of an extended energy function, including a new type of distance restraint and a term depending on the data quality, results in an estimation of the data quality in addition to coordinates. This allows for the determination of the optimal weight on the experimental information. The resulting structures are of better quality and closer to the X-ray crystal structure of the same molecule. With the new calculation approach, the analysis of discrepancies from the target distances becomes meaningful. The strategy may be useful in other applications-for example, in homology modeling.

Graphical analysis of NMR structural quality and interactive contact map of NOE assignments in ARIA.

BACKGROUND: The Ambiguous Restraints for Iterative Assignment (ARIA) approach is widely used for NMR structure determination. It is based on simultaneously calculating structures and assigning NOE through an iterative protocol. The final solution consists of a set of conformers and a list of most probable assignments for the input NOE peak list. RESULTS: ARIA was extended with a series of graphical tools to facilitate a detailed analysis of the intermediate and final results of the ARIA protocol. These additional features provide (i) an interactive contact map, serving as a tool for the analysis of assignments, and (ii) graphical representations of structure quality scores and restraint statistics. The interactive contact map between residues can be clicked to obtain information about the restraints and their contributions. Profiles of quality scores are plotted along the protein sequence, and contact maps provide information of the agreement with the data on a residue pair level. CONCLUSION: The graphical tools and outputs described here significantly extend the validation and analysis possibilities of NOE assignments given by ARIA as well as the analysis of the quality of the final structure ensemble. These tools are included in the latest version of ARIA, which is available at http://aria.pasteur.fr. The Web site also contains an installation guide, a user manual and example calculations.

Three-dimensional solution structure and conformational plasticity of the N-terminal scavenger receptor cysteine-rich domain of human CD5.

The lymphocyte receptor CD5 influences cell activation by modifying the strength of the intracellular response initiated by antigen engagement. Regulation through CD5 involves the interaction of one or more of its three scavenger receptor cysteine-rich domains present in the extracellular region. Here, we present the 3D solution structure of a non-glycosylated double mutant of the N-terminal domain of human CD5 expressed in Escherichia coli (eCD5d1m), which has enhanced solubility compared to the non-glycosylated wild-type (eCD5d1). In common with a glycosylated form expressed in Pichia pastoris, the [(15)N,(1)H]-correlation spectra of both eCD5d1 and eCD5d1m exhibit non-uniform temperature-dependent signal intensities, indicating extensive conformational fluctuations on the micro-millisecond timescale. Although approximately one half of the signals expected for the domain are absent at 298 K, essentially complete resonance assignments and a solution structure could be obtained at 318 K. Because of the sparse nature of the experimental restraint data and the potentially important contribution of conformational exchange to the nuclear Overhauser effect peak intensity, we applied inferential structure determination to calculate the eCD5d1m structure. The inferential structure determination ensemble has similar features to that obtained by traditional simulated annealing methods, but displays superior definition and structural quality. The eCD5d1m structure is similar to other members of the scavenger receptor cysteine-rich superfamily, but the position of the lone alpha helix differs due to interactions with the unique N-terminal region of the domain. The availability of an experimentally tractable form of CD5d1, together with its 3D structure, provides new tools for further investigation of its function within intact CD5.

ISD: a software package for Bayesian NMR structure calculation.

UNLABELLED: The conventional approach to calculating biomolecular structures from nuclear magnetic resonance (NMR) data is often viewed as subjective due to its dependence on rules of thumb for deriving geometric constraints and suitable values for theory parameters from noisy experimental data. As a result, it can be difficult to judge the precision of an NMR structure in an objective manner. The inferential structure determination (ISD) framework, which has been introduced recently, addresses this problem by using Bayesian inference to derive a probability distribution that represents both the unknown structure and its uncertainty. It also determines additional unknowns, such as theory parameters, that normally need to be chosen empirically. Here we give an overview of the ISD software package, which implements this methodology. AVAILABILITY: http://www.bioc.cam.ac.uk/isd

Comparative analysis of structural and dynamic properties of the loaded and unloaded hemophore HasA: functional implications.

A heme-acquisition system present in several Gram-negative bacteria requires the secretion of hemophores. These extracellular carrier proteins capture heme and deliver it to specific outer membrane receptors. The Serratia marcescens HasA hemophore is a monodomain protein that binds heme with a very high affinity. Its alpha/beta structure, as that of its binding pocket, has no common features with other iron- or heme-binding proteins. Heme is held by two loops L1 and L2 and coordinated to iron by an unusual ligand pair, H32/Y75. Two independent regions of the hemophore beta-sheet are involved in HasA-HasR receptor interaction. Here, we report the 3-D NMR structure of apoHasA and the backbone dynamics of both loaded and unloaded hemophore. While the overall structure of HasA is very similar in the apo and holo forms, the hemophore presents a transition from an open to a closed form upon ligand binding, through a large movement, of up to 30 A, of loop L1 bearing H32. Comparison of loaded and unloaded HasA dynamics on different time scales reveals striking flexibility changes in the binding pocket. We propose a mechanism by which these structural and dynamic features provide the dual function of heme binding and release to the HasR receptor.

A unifying probabilistic framework for analyzing residual dipolar couplings.

Residual dipolar couplings provide complementary information to the nuclear Overhauser effect measurements that are traditionally used in biomolecular structure determination by NMR. In a de novo structure determination, however, lack of knowledge about the degree and orientation of molecular alignment complicates the analysis of dipolar coupling data. We present a probabilistic framework for analyzing residual dipolar couplings and demonstrate that it is possible to estimate the atomic coordinates, the complete molecular alignment tensor, and the error of the couplings simultaneously. As a by-product, we also obtain estimates of the uncertainty in the coordinates and the alignment tensor. We show that our approach encompasses existing methods for determining the alignment tensor as special cases, including least squares estimation, histogram fitting, and elimination of an explicit alignment tensor in the restraint energy.

ARIA2: automated NOE assignment and data integration in NMR structure calculation.

UNLABELLED: Modern structural genomics projects demand for integrated methods for the interpretation and storage of nuclear magnetic resonance (NMR) data. Here we present version 2.1 of our program ARIA (Ambiguous Restraints for Iterative Assignment) for automated assignment of nuclear Overhauser enhancement (NOE) data and NMR structure calculation. We report on recent developments, most notably a graphical user interface, and the incorporation of the object-oriented data model of the Collaborative Computing Project for NMR (CCPN). The CCPN data model defines a storage model for NMR data, which greatly facilitates the transfer of data between different NMR software packages. AVAILABILITY: A distribution with the source code of ARIA 2.1 is freely available at http://www.pasteur.fr/recherche/unites/Binfs/aria2.

Error distribution derived NOE distance restraints.

Errors and imprecisions in distance restraints derived from NOESY peak volumes are usually accounted for by generous lower and upper bounds on the distances. In this paper, we propose a new form of distance restraints, replacing the subjective bounds by a potential function obtained from the error distribution of the distances. We derived the shape of the potential from molecular dynamics calculations and by comparison of NMR data with X-ray crystal structures. We used complete cross-validation to derive the optimal weight for the data in the calculation. In a model system with synthetic restraints, the accuracy of the structures improved significantly compared to calculations with the usual form of restraints. For experimental data sets, the structures systematically approach the X-ray crystal structures of the same protein. Also standard quality indicators improve compared to standard calculations. The results did not depend critically on the exact shape of the potential. The new approach is less subjective and uses fewer assumptions in the interpretation of NOESY peak volumes as distance restraints than the usual approach. Figures of merit for the structures, such as the RMS difference from the average structure or the RMS difference from the data, are therefore less biased and more meaningful measures of structure quality than with the usual form of restraints.

Weighting of experimental evidence in macromolecular structure determination.

The determination of macromolecular structures requires weighting of experimental evidence relative to prior physical information. Although it can critically affect the quality of the calculated structures, experimental data are routinely weighted on an empirical basis. At present, cross-validation is the most rigorous method to determine the best weight. We describe a general method to adaptively weight experimental data in the course of structure calculation. It is further shown that the necessity to define weights for the data can be completely alleviated. We demonstrate the method on a structure calculation from NMR data and find that the resulting structures are optimal in terms of accuracy and structural quality. Our method is devoid of the bias imposed by an empirical choice of the weight and has some advantages over estimating the weight by cross-validation.

Modeling errors in NOE data with a log-normal distribution improves the quality of NMR structures.

The distribution of the deviation of calculated from measured nuclear Overhauser effect (NOE) intensities is a priori unknown. The use of a log-normal distribution to describe these deviations permits the direct calculation of a structure from the measured intensities without first converting them into distance bounds. We show that the log-normal distribution is a natural choice for describing errors in NOE data and that it improves the accuracy, precision, and quality of the calculated structures compared to the usual bounds representation.

Bayesian inference applied to macromolecular structure determination.

The determination of macromolecular structures from experimental data is an ill-posed inverse problem. Nevertheless, conventional techniques to structure determination attempt an inversion of the data by minimization of a target function. This approach leads to problems if the data are sparse, noisy, heterogeneous, or difficult to describe theoretically. We propose here to view biomolecular structure determination as an inference rather than an inversion problem. Probability theory then offers a consistent formalism to solve any structure determination problem: We use Bayes' theorem to derive a probability distribution for the atomic coordinates and all additional unknowns. This distribution represents the complete information contained in the data and can be analyzed numerically by Markov chain Monte Carlo sampling techniques. We apply our method to data obtained from a nuclear magnetic resonance experiment and discuss the estimation of theory parameters.

Bayesian estimation of Karplus parameters and torsion angles from three-bond scalar couplings constants.

We apply Bayesian inference to analyze three-bond scalar coupling constants in an objective and consistent way. The Karplus curve and a Gaussian error law are used to model scalar coupling measurements. By applying Bayes' theorem, we obtain a probability distribution for all unknowns, i.e., the torsion angles, the Karplus parameters, and the standard deviation of the Gaussian. We infer all these unknowns from scalar coupling data using Markov chain Monte Carlo sampling and analytically derive a probability distribution that only involves the torsion angles.

Inferential structure determination.

Macromolecular structures calculated from nuclear magnetic resonance data are not fully determined by experimental data but depend on subjective choices in data treatment and parameter settings. This makes it difficult to objectively judge the precision of the structures. We used Bayesian inference to derive a probability distribution that represents the unknown structure and its precision. This probability distribution also determines additional unknowns, such as theory parameters, that previously had to be chosen empirically. We implemented this approach by using Markov chain Monte Carlo techniques. Our method provides an objective figure of merit and improves structural quality.

Replica-exchange Monte Carlo scheme for bayesian data analysis.

We develop a sampling algorithm to explore the probability densities arising in Bayesian data analysis problems. Our algorithm is a multiparameter generalization of a replica-exchange Monte Carlo scheme. The strategy relies on gradual weighing of experimental data and on Tsallis generalized statistics. We demonstrate the effectiveness of the method on nuclear magnetic resonance data for a folded protein.

NOE assignment with ARIA 2.0: the nuts and bolts.

The assignment of nuclear Overhauser effect (NOE) resonances is the crucial step in determining the three-dimensional structure of biomolecules from nuclear magnetic resonance (NMR) data. Our program, Ambiguous Restraints for Iterative Assignment (ARIA), treats Noe assignment as an integral part of the structure determination process. This chapter briefly outlines the method and discusses how to carry out a complete structure determination project with the new version 2.0 of ARIA. Two new features greatly streamline the procedure: a new graphical user interface (GUI) and the incorporation of the data model of the Collaborative Computing Project for the NMR community (CCPN). The GUI supports the user in setting up and managing a project. The CCPN data model facilitates data exchange with a great variety of other programs. We give practical guidelines for how to use ARIA and how to analyze results.

Correction of spin diffusion during iterative automated NOE assignment.

Indirect magnetization transfer increases the observed nuclear Overhauser enhancement (NOE) between two protons in many cases, leading to an underestimation of target distances. Wider distance bounds are necessary to account for this error. However, this leads to a loss of information and may reduce the quality of the structures generated from the inter-proton distances. Although several methods for spin diffusion correction have been published, they are often not employed to derive distance restraints. This prompted us to write a user-friendly and CPU-efficient method to correct for spin diffusion that is fully integrated in our program ambiguous restraints for iterative assignment (ARIA). ARIA thus allows automated iterative NOE assignment and structure calculation with spin diffusion corrected distances. The method relies on numerical integration of the coupled differential equations which govern relaxation by matrix squaring and sparse matrix techniques. We derive a correction factor for the distance restraints from calculated NOE volumes and inter-proton distances. To evaluate the impact of our spin diffusion correction, we tested the new calibration process extensively with data from the Pleckstrin homology (PH) domain of Mus musculus beta-spectrin. By comparing structures refined with and without spin diffusion correction, we show that spin diffusion corrected distance restraints give rise to structures of higher quality (notably fewer NOE violations and a more regular Ramachandran map). Furthermore, spin diffusion correction permits the use of tighter error bounds which improves the distinction between signal and noise in an automated NOE assignment scheme.

ARIA: automated NOE assignment and NMR structure calculation.

MOTIVATION: In the light of several ongoing structural genomics projects, faster and more reliable methods for structure calculation from NMR data are in great demand. The major bottleneck in the determination of solution NMR structures is the assignment of NOE peaks (nuclear Overhauser effect). Due to the high complexity of the assignment problem, most NOEs cannot be directly converted into unambiguous inter-proton distance restraints. RESULTS: We present version 1.2 of our program ARIA (Ambiguous Restraints for Iterative Assignment) for automated assignment of NOE data and NMR structure calculation. We summarize recent progress in correcting for spin diffusion with a relaxation matrix approach, representing non-bonded interactions in the force field and refining final structures in explicit solvent. We also discuss book-keeping, data exchange with spectra assignment programs and deposition of the analysed experimental data to the databases. AVAILABILITY: ARIA 1.2 is available from: http://www.pasteur.fr/recherche/unites/Binfs/aria/. SUPPLEMENTARY INFORMATION: XML DTDs (for chemical shifts and NOE crosspeaks), Python scripts for the conversion of various NMR data formats and the results of example calculations using data from the S. cerevisiae HRDC domain are available from: http://www.pasteur.fr/recherche/unites/Binfs/aria/