Wolfgang Rieping

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.

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/

The CCPN project: an interim report on a data model for the NMR community.

A recent workshop discusses the progress toward integrating NMR data into a unifying data model.

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.

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.

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. Proteins 2006.

A new principle for macromolecular structure determination

Protein NMR spectroscopy is a modern experimental technique for elucidating the three‐dimensional structure of biological macromolecules in solution. From the data‐analytical point of view, structure determination has always been considered an optimisation problem: much effort has been spent on the development of minimisation strategies; the underlying rationale, however, has not been revised. Conceptual difficulties with this approach arise since experiments only provide incomplete structural information: structure determination is an inference problem and demands for a probabilistic treatment. In order to generate realistic conformations, strong prior assumptions about physical interactions are indispensable. These interactions impose a complex structure on the posterior distribution making simulation of such models particularly difficult. We demonstrate, that posterior sampling is feasible using a combination of multiple Markov Chain Monte Carlo techniques. We apply the methodology to a sparse data set...

Structure determination from heterogeneous NMR data

The principal difficulty in using nuclear magnetic resonance (NMR) data for biomolecular structure determination is not so much experimental imperfections but approximate theories relating structure to measurands. Furthermore, these theories are incomplete as they involve auxiliary parameters which are not measurable. In order to give a reliable picture of a biomolecule, structure determination methods need to determine unknown parameters from definite rules and ought to provide the uncertainty of the derived coordinates. Conventional approaches neglect uncertainties of any kind and therefore by definition fail to give an estimate of structural reliability. In order to deal with auxiliary parameters, they resort to heuristics which renders an objective interpretation of the generated atom positions impossible. Recently, we have introduced a fully probabilistic approach to structure determination from NMR data. We describe here an extension of this approach which incorporates inconsistent nuclear Overhause...

Estimation of proton configurations from NOESY spectra

Nuclear Overhauser effect spectroscopy (NOESY) data contain information about the geometry of a system of magnetically interacting nuclear spins. They thus allow to determine the positions of atoms in space and even suffice to infer the entire structure of a biomolecule. Yet, interpretation of NOESY spectra is often still qualitative. This is mostly due to the complexity of the theories that describe the data. We outline a Bayesian algorithm that estimates the configuration of protons by analysing unassigned NOESY volumes which may stem from spectra recorded at different mixing times. The method relies on the calculation of peak volumes with a relaxation matrix model and thereby incorporates spin‐diffusion effects. Proton coordinates and nuisance parameters, such as the scales and errors of the spectra, are estimated using Markov Chain Monte Carlo sampling.