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Structural information about ribonucleic acid (RNA) is lagging behind that of proteins, in part due to its high charge and conformational variability. Molecular dynamics (MD) has played an important role in describing RNA structure, complementing information from both nuclear magnetic resonance (NMR), or X-ray crystallography. We examine the impact of the choice of the empirical force field for RNA structure refinement using cross-validation against residual dipolar couplings (RDCs) as structural accuracy reporter. Four force fields, representing both the state-of-the art in RNA simulation and the most popular selections in NMR structure determination, are compared for a prototypical A-RNA helix. RNA structural accuracy is also evaluated as a function of both density and nature of input NMR data including RDCs, anisotropic chemical shifts, and distance restraints. Our results show a complex interplay between the experimental restraints and the force fields indicating two best-performing choices: high-fidelity refinement in explicit solvent, and the conformational database-derived potentials. Accuracy of RNA models closely tracks the density of 1-bond C-H RDCs, with other data types having beneficial, but smaller effects. At lower RDC density, or when refining against NOEs only, the two selected force fields are capable of accurately describing RNA helices with little or no experimental RDC data, making them available for the higher order structure assembly or better quantification of the intramolecular dynamics. Unrestrained simulations of simple RNA motifs with state-of-the art MD force fields appear to capture the flexibility inherent in nucleic acids while also maintaining a good agreement with the experimental observables.

A procedure is presented for refinement of a homology model of E. coli tRNA(Val), originally based on the X-ray structure of yeast tRNA(Phe), using experimental residual dipolar coupling (RDC) and small angle X-ray scattering (SAXS) data. A spherical sampling algorithm is described for refinement against SAXS data that does not require a globbic approximation, which is particularly important for nucleic acids where such approximations are less appropriate. Substantially higher speed of the algorithm also makes its application favorable for proteins. In addition to the SAXS data, the structure refinement employed a sparse set of NMR data consisting of 24 imino N-H(N) RDCs measured with Pf1 phage alignment, and 20 imino N-H(N) RDCs obtained from magnetic field dependent alignment of tRNA(Val). The refinement strategy aims to largely retain the local geometry of the 58% identical tRNA(Phe) by ensuring that the atomic coordinates for short, overlapping segments of the ribose-phosphate backbone and the conserved base pairs remain close to those of the starting model. Local coordinate restraints are enforced using the non-crystallographic symmetry (NCS) term in the XPLOR-NIH or CNS software package, while still permitting modest movements of adjacent segments. The RDCs mainly drive the relative orientation of the helical arms, whereas the SAXS restraints ensure an overall molecular shape compatible with experimental scattering data. The resulting structure exhibits good cross-validation statistics (jack-knifed Q (free) = 14% for the Pf1 RDCs, compared to 25% for the starting model) and exhibits a larger angle between the two helical arms than observed in the X-ray structure of tRNA(Phe), in agreement with previous NMR-based tRNA(Val) models.

Determination of the accurate three-dimensional structure of large proteins by NMR remains challenging due to a loss in the density of experimental restraints resulting from the often prerequisite perdeuteration. Solution small-angle scattering, which carries long-range translational information, presents an opportunity to enhance the structural accuracy of derived models when used in combination with global orientational NMR restraints such as residual dipolar couplings (RDCs) and residual chemical shift anisotropies (RCSAs). We have quantified the improvements in accuracy that can be obtained using this strategy for the 82 kDa enzyme Malate Synthase G (MSG), currently the largest single chain protein solved by solution NMR. Joint refinement against NMR and scattering data leads to an improvement in structural accuracy as evidenced by a decrease from approximately 4.5 to approximately 3.3 A of the backbone rmsd between the derived model and the high-resolution X-ray structure, PDB code 1D8C. This improvement results primarily from medium-angle scattering data, which encode the overall molecular shape, rather than the lowest angle data that principally determine the radius of gyration and the maximum particle dimension. The effect of the higher angle data, which are dominated by internal density fluctuations, while beneficial, is also found to be relatively small. Our results demonstrate that joint NMR/SAXS refinement can yield significantly improved accuracy in solution structure determination and will be especially well suited for the study of systems with limited NMR restraints such as large proteins, oligonucleotides, or their complexes.

For base-paired nucleic acids, variations in (1) J (NH) and the imino (1)H chemical shift are both dominated by hydrogen bond length. In the absence of molecular alignment, the (1) J (NH) coupling for the imino proton then can be approximated by (1) J (NH) = (1.21Hz/ppm)delta(H) - 103.5 +/- 0.6 Hz, where delta(H) represents the chemical shift of the imino proton in ppm. This relation permits imino residual dipolar couplings (RDCs) resulting from magnetic susceptibility anisotropy (MSA) to be extracted from measurement of ((1) J (NH) + RDC) splittings at a single magnetic field strength. Magnetic field-induced RDCs were measured for tRNA(Val) and the alignment tensor determined from magnetic-field alignment of tRNA(Val) agrees well with the tensor calculated by summation of the MSA tensors of the individual nucleobases.

NMR frequency assignments are usually considered a prerequisite for the analysis of NOESY spectra, in turn required for the calculation of biomolecular structures. In contrast, as we propose here, relatively high numbers of unambiguous NOE identities can be consistently achieved in an automated manner by relying only on grouping resonances into connected spin systems. To achieve this goal, we have developed for proteins two protocols, SPI and BACUS, based on Bayesian inference. SPI (Grishaev and Llinás, 2002c) produces a list of the (1)H resonance frequencies from homo- and hetero-nuclear multidimensional spectra, grouped into effective spin systems. BACUS automatically establishes probabilistic identities of NOESY cross-peaks in terms of the chemical shifts provided by SPI. BACUS requires neither assignment of resonances nor an initial structural model. It successfully copes with chemical shift overlap and does so without cycling through 3D structure calculations. The method exploits the self-consistency of the NOESY graph by taking advantage of a network of J- as well as NOE-connected "reporter" protons sorted via SPI. BACUS was validated by tests on experimental NOESY data recorded for the col 2 and kringle 2 domains.

Grouping of spectral peaks into J-connected spin systems is essential in the analysis of macromolecular NMR data as it provides the basis for disentangling chemical shift degeneracies. It is a mandatory step before resonance and NOESY cross-peak identities can be established. We have developed SPI, a computational protocol that scrutinizes peak lists from homo- and hetero-nuclear multidimensional NMR spectra and progressively assembles sets of resonances into consensus J- and/or NOE-connected spin systems. SPI estimates the likelihood of nuclear spin resonances appearing at defined frequencies given sets of cross-peaks measured from multi-dimensional experiments. It quantifies spin system matching probabilities via Bayesian inference. The protocol takes advantage of redundancies in the number of connectivities revealed by suites of diverse NMR experiments, systematically tracking the adequacy of each grouping hypothesis. SPI was tested on 2D homonuclear and 2D/3D(15)N-edited data recorded from two protein modules, the col 2 domain of matrix metalloproteinase-2 (MMP-2) and the kringle 2 domain of plasminogen, of 60 and 83 amino acid residues, respectively. For these protein domains SPI identifies approximately 95% unambiguous resonance frequencies, a relatively good performance vis-a-vis the reported 'manual' (interactive) analyses. Abbreviations and Acronyms: SPI, SPin Identification; BMRB, BioMagResBank (Madison, WI).

Grouping of spectral peaks into J-connected spin systems is essential in the analysis of macromolecular NMR data as it provides the basis for disentangling chemical shift degeneracies. It is a mandatory step before resonance and NOESY cross-peak identities can be established. We have developed SPI, a computational protocol that scrutinizes peak lists from homo- and hetero-nuclear multidimensional NMR spectra and progressively assembles sets of resonances into consensus J- and/or NOE-connected spin systems. SPI estimates the likelihood of nuclear spin resonances appearing at defined frequencies given sets of cross-peaks measured from multi-dimensional experiments. It quantifies spin system matching probabilities via Bayesian inference. The protocol takes advantage of redundancies in the number of connectivities revealed by suites of diverse NMR experiments, systematically tracking the adequacy of each grouping hypothesis. SPI was tested on 2D homonuclear and 2D/3D(15)N-edited data recorded from two protein modules, the col 2 domain of matrix metalloproteinase-2 (MMP-2) and the kringle 2 domain of plasminogen, of 60 and 83 amino acid residues, respectively. For these protein domains SPI identifies approximately 95% unambiguous resonance frequencies, a relatively good performance vis-à-vis the reported 'manual' (interactive) analyses. Abbreviations and Acronyms: SPI, SPin Identification; BMRB, BioMagResBank (Madison, WI).