Computational Methods for RNA Structure Validation and Improvement

Jain, Swati; Richardson, David C.; Richardson, Jane S.

doi:10.1016/bs.mie.2015.01.007

Cited by 29 publications

(29 citation statements)

References 55 publications

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“…The reader should be aware that when comparing NMR, MD simulations, and X-ray structures, differences exist between the specific BII percentages assigned to some base pairs steps 12,21 . The sources of uncertainties from crystal structures have being discussed several times and are clearly related to low resolution (frequently insufficient to define backbone's states), lack of dynamics, and lattice restraints 13,25,26 . In the same way, changes in NMR's refinement protocols or annealing procedures from one experiment to another 27,28 , the usually low number of restraints due to the low density of protons in DNA, and the frequent overlap of several NOE peaks 20 , are the main sources of uncertainties, being the average standard deviation in the prediction of BII percentages ±8 21 .…”

Section: Resultsmentioning

confidence: 99%

The Role of Unconventional Hydrogen Bonds in Determining BII Propensities in B-DNA

Balaceanu

Pasi

Dans

et al. 2016

J. Phys. Chem. Lett.

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An accurate understanding of DNA backbone transitions is likely to be the key for elucidating the puzzle of the intricate sequence-dependent mechanical properties that govern most of the biologically relevant functions of the double helix. One factor believed to be important in indirect recognition within protein-DNA complexes is the combined effect of two DNA backbone torsions (ε and ζ) which give rise to the well-known BI/BII conformational equilibrium. In this work we explain the sequence dependent BII propensity observed in RpY steps (R = purine; Y = pyrimidine) at the tetranucleotide level with the help of a previously undetected C-H···O contact between atoms belonging to adjacent bases. Our results are supported by extensive multi-microsecond molecular dynamics simulations from the Ascona B-DNA Consortium, high-level quantum mechanical calculations, and data mining of the experimental structures deposited in the Protein Data Bank.

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Section: Resultsmentioning

confidence: 99%

The Role of Unconventional Hydrogen Bonds in Determining BII Propensities in B-DNA

Balaceanu

Pasi

Dans

et al. 2016

J. Phys. Chem. Lett.

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“…Wide angle X-ray scattering (WAXS), where q > 0.3 Å −1 , contains even finer structural information such as nucleic acid helical groove width [7] and in favorable cases can even resolve the spacing of phosphate groups between base pairs [11]. Additionally, significant advances have been made in computational modeling of RNA structure, including the validation and improvement of high resolution models [12], generation of models from chemical mapping [13], and de novo structure prediction approaches [14]. Until recently, X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy were the only experimental methods capable of elucidating molecular structures at the atomic level.…”

Section: Introductionmentioning

confidence: 99%

Structural Analysis of Multi-Helical RNAs by NMR–SAXS/WAXS: Application to the U4/U6 di-snRNA

Cornilescu

Didychuk

Rodgers

et al. 2016

Journal of Molecular Biology

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NMR and SAXS/WAXS are highly complementary approaches for the analysis of RNA structure in solution. Here we describe an efficient NMR-SAXS/WAXS approach for structural investigation of multi-helical RNAs. We illustrate this approach by determining the overall fold of a 92-nucleotide 3-helix junction from the U4/U6 di-snRNA. The U4/U6 di-snRNA is conserved in eukaryotes and is part of the U4/U6.U5 tri-snRNP, a large ribonucleoprotein complex that comprises a major subunit of the assembled spliceosome. Helical orientations can be determined by X-ray scattering data alone, but the addition of NMR RDC restraints improves the structure models. RDCs were measured in 2 different external alignment media and also by magnetic susceptibility anisotropy. The resulting alignment tensors are collinear, which is a previously noted problem for nucleic acids. Including WAXS data in the calculations produces models with significantly better fits to the scattering data. In solution, the U4/U6 di-snRNA forms a 3-helix junction with a planar Y-shaped structure and has no detectable tertiary interactions. Single molecule FRET data support the observed topology. A comparison with the recently determined cryo-EM structure of the U4/U6.U5 tri-snRNP illustrates how proteins scaffold the RNA and dramatically alter the geometry of the U4/U6 3-helix junction.

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“…mation is crucial to all of these functions, but has too many variables to model straightforwardly at the usual resolutions attainable, where the backbone between ribose and phosphate is a featureless tube. Fortunately, there are some tools to help with this problem (Jain et al, 2015).…”

Section: Rnamentioning

confidence: 99%

“…Bond-length and angle parameters were pioneered by Engh & Huber (1991) for proteins and by Berman and Olson (Gelbin et al, 1996) for nucleic acids; they are shown here as springs or fans, in red if too broad and in blue if too short. They have been updated primarily by making them context-dependent: on ', by Karplus and Dunbrack (Berkholz et al, 2009), on ribose pucker by us (Jain et al, 2015) or by combining angles into the C deviation, which flags a C position forced to be nontetrahedral by an incorrect local fit of either the side chain or the backbone (Lovell et al, 2003). Outliers in conformation (combinations of rotatable dihedrals) are shown in gold for side-chain rotamers, in green for Ramachandran and as magenta crosses for ribose pucker.…”

Section: Updates Of Traditional Validation Measuresmentioning

confidence: 99%

Model validation: local diagnosis, correction and when to quit

Richardson

Williams

Hintze

et al. 2018

Acta Crystallogr D Struct Biol

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Traditionally, validation was considered to be a final gatekeeping function, but refinement is smoother and results are better if model validation actively guides corrections throughout structure solution. This shifts emphasis from global to local measures: primarily geometry, conformations and sterics. A fit into the wrong local minimum conformation usually produces outliers in multiple measures. Moving to the right local minimum should be prioritized, rather than small shifts across arbitrary borderlines. Steric criteria work best with all explicit H atoms. `Backrub' motions should be used for side chains and `P-perp' diagnostics to correct ribose puckers. A `water' may actually be an ion, a relic of misfitting or an unmodeled alternate. Beware of wishful thinking in modeling ligands. At high resolution, internally consistent alternate conformations should be modeled and geometry in poor density should not be downweighted. At low resolution,CaBLAMshould be used to diagnose protein secondary structure andERRASERto correct RNA backbone. All atoms should not be forced inside density, beware of sequence misalignment, and very rare conformations such ascis-non-Pro peptides should be avoided. Automation continues to improve, but the crystallographer still must look at each outlier, in the context of density, and correct most of them. For the valid few with unambiguous density and something that is holding them in place, a functional reason should be sought. The expectation is a few outliers, not zero.

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Computational Methods for RNA Structure Validation and Improvement

Cited by 29 publications

References 55 publications

The Role of Unconventional Hydrogen Bonds in Determining BII Propensities in B-DNA

The Role of Unconventional Hydrogen Bonds in Determining BII Propensities in B-DNA

Structural Analysis of Multi-Helical RNAs by NMR–SAXS/WAXS: Application to the U4/U6 di-snRNA

Model validation: local diagnosis, correction and when to quit

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