RNA Backbone Validation, Correction, and Implications for RNA-Protein Interfaces

<p>RNA is the molecular workhorse of nature, capable of doing many cellular tasks, from genetic data storage and regulation, to enzymatic synthesis--even to the point of self-catalyzing its own replication. While RNA can act as a catalyst on its own, as in the hammerhead ribozyme, the added efficiency of proteins is often a necessity; the ribosome--the large ribozyme responsible for peptide chain formation, is aided by proteins which ensure correct assembly and structural stability. These complexes of RNA and proteins feature in many essential cellular processes, including the RISC silencing complex and in the spliceosome. Despite its enormous utility, structural determination of RNA is notoriously difficult--particularly in the backbone, since a nucleotide standardly has 12 torsion angles (including &#967;) and 12 non-hydrogen atoms, compared to 4 torsions (including &#967;1) and 4 non-H atoms in a typical amino acid. The abundance of backbone atoms, their conformational flexibility, and experimental resolution limitations often result in systematic errors that can have a significant impact on the interpretation. False trails due to structural errors can lead to significant loss of time and effort, especially with such high-profile complexes as the ribosome and the RISC complex. </p><p>My research has focused on harnessing the recently discovered ribosome structures and the Richardsons' RNA dataset to find trends in RNA backbone conformations and motifs that were then used to develop structural validation techniques and provide improved diagnosis and correction techniques for RNA backbone. Methods for fixing RNA structure have been developed for both NMR and X-ray crystallography. For NMR structures, a method for assigning RNA backbone structure based on NOE data was developed, leading to improved identification and building of RNA backbone conformation in NMR ensembles. For crystallography, our method of diagnosing the correct ribose pucker from clear observables allows reliable assessment of pucker in validation or refinement. Observed differences in bond-lengths, bond-angles, and dihedrals have been categorized by sugar pucker in the PHENIX refinement package. I have shown that this improves the refinement behavior of both pucker and geometry. </p><p>There have also been improvements in identifying structural motifs. Many previously identified structural motifs have now been defined in terms of backbone suitestrings, a series of 2-character code divisions of RNA backbone that show the best clustering of dihedral angle correlations. Combined with a BLAST-like alignment program called SuiteAlign, these suitestrings were quickly and easily identified in a number of structures, eventually leading to the discovery of multiple instances of T&#968;C-loop structures in the ribosome.</p><p>To facilitate error diagnosis and corrections in RNA-protein complexes, as well as to expand the knowledge base of the scientific community as a whole, a database of RNA-protein interaction motifs has been developed. This database is rooted in the quality-filtering, visualization, and analysis techniques of the Richardson lab, particularly those developed by Laura Murray specifically for RNA structures.</p><p>The consensus backbone conformers, pucker diagnosis, and all-atom contacts have been combined to develop first manual and then automated tools for RNA structure correction. I have applied all these techniques to improve the accuracy of a number of important RNA and RNA/protein complex structures.</p>Dissertatio

RNABC: forward kinematics to reduce all-atom steric clashes in RNA backbone

Although accurate details in RNA structure are of great importance for understanding RNA function, the backbone conformation is difficult to determine, and most existing RNA structures show serious steric clashes (≥ 0.4Å overlap) when hydrogen atoms are taken into account. We have developed a program called RNABC (RNA Backbone Correction) that performs local perturbations to search for alternative conformations that avoid those steric clashes or other local geometry problems. Its input is an all-atom coordinate file for an RNA crystal structure (usually from the MolProbity web service), with problem areas specified. RNABC rebuilds a suite (the unit from sugar to sugar) by anchoring the phosphorus and base positions, which are clearest in crystallographic electron density, and reconstructing the other atoms using forward kinematics. Geometric parameters are constrained within user-specified tolerance of canonical or original values, and torsion angles are constrained to ranges defined through empirical database analyses. Several optimizations reduce the time required to search the many possible conformations. The output results are clustered and presented to the user, who can choose whether to accept one of the alternative conformations

MolProbity: all-atom contacts and structure validation for proteins and nucleic acids

MolProbity is a general-purpose web server offering quality validation for 3D structures of proteins, nucleic acids and complexes. It provides detailed all-atom contact analysis of any steric problems within the molecules as well as updated dihedral-angle diagnostics, and it can calculate and display the H-bond and van der Waals contacts in the interfaces between components. An integral step in the process is the addition and full optimization of all hydrogen atoms, both polar and nonpolar. New analysis functions have been added for RNA, for interfaces, and for NMR ensembles. Additionally, both the web site and major component programs have been rewritten to improve speed, convenience, clarity and integration with other resources. MolProbity results are reported in multiple forms: as overall numeric scores, as lists or charts of local problems, as downloadable PDB and graphics files, and most notably as informative, manipulable 3D kinemage graphics shown online in the KiNG viewer. This service is available free to all users at http://molprobity.biochem.duke.edu

MolProbity: all-atom structure validation for macromolecular crystallography

MolProbity structure validation will diagnose most local errors in macromolecular crystal structures and help to guide their correction

PHENIX: a comprehensive Python-based system for macromolecular structure solution.

Macromolecular X-ray crystallography is routinely applied to understand biological processes at a molecular level. However, significant time and effort are still required to solve and complete many of these structures because of the need for manual interpretation of complex numerical data using many software packages and the repeated use of interactive three-dimensional graphics. PHENIX has been developed to provide a comprehensive system for macromolecular crystallographic structure solution with an emphasis on the automation of all procedures. This has relied on the development of algorithms that minimize or eliminate subjective input, the development of algorithms that automate procedures that are traditionally performed by hand and, finally, the development of a framework that allows a tight integration between the algorithms

New tools provide a second look at HDV ribozyme structure, dynamics and cleavage.

The hepatitis delta virus (HDV) ribozyme is a self-cleaving RNA enzyme essential for processing viral transcripts during rolling circle viral replication. The first crystal structure of the cleaved ribozyme was solved in 1998, followed by structures of uncleaved, mutant-inhibited and ion-complexed forms. Recently, methods have been developed that make the task of modeling RNA structure and dynamics significantly easier and more reliable. We have used ERRASER and PHENIX to rebuild and re-refine the cleaved and cis-acting C75U-inhibited structures of the HDV ribozyme. The results correct local conformations and identify alternates for RNA residues, many in functionally important regions, leading to improved R values and model validation statistics for both structures. We compare the rebuilt structures to a higher resolution, trans-acting deoxy-inhibited structure of the ribozyme, and conclude that although both inhibited structures are consistent with the currently accepted hammerhead-like mechanism of cleavage, they do not add direct structural evidence to the biochemical and modeling data. However, the rebuilt structures (PDBs: 4PR6, 4PRF) provide a more robust starting point for research on the dynamics and catalytic mechanism of the HDV ribozyme and demonstrate the power of new techniques to make significant improvements in RNA structures that impact biologically relevant conclusions

Small Molecule-Based Pattern Recognition To Classify RNA Structure

Three-dimensional RNA structures are notoriously difficult to determine, and the link between secondary structure and RNA conformation is only beginning to be understood. These challenges have hindered the identification of guiding principles for small molecule:RNA recognition. We herein demonstrate that the strong and differential binding ability of aminoglycosides to RNA structures can be used to classify five canonical RNA secondary structure motifs through principal component analysis (PCA). In these analyses, the aminoglycosides act as receptors, while RNA structures labeled with a benzofuranyluridine fluorophore act as analytes. Complete (100%) predictive ability for this RNA training set was achieved by incorporating two exhaustively guanidinylated aminoglycosides into the receptor library. The PCA was then externally validated using biologically relevant RNA constructs. In bulge-stem-loop constructs of HIV-1 transactivation response element (TAR) RNA, we achieved nucleotide-specific classification of two independent secondary structure motifs. Furthermore, examination of cheminformatic parameters and PCA loading factors revealed trends in aminoglycoside:RNA recognition, including the importance of shape-based discrimination, and suggested the potential for size and sequence discrimination within RNA structural motifs. These studies present a new approach to classifying RNA structure and provide direct evidence that RNA topology, in addition to sequence, is critical for the molecular recognition of RNA