Analysis and classification of RNA tertiary structures

  1. Mira Abraham1,
  2. Oranit Dror1,
  3. Ruth Nussinov2,3, and
  4. Haim J. Wolfson1
  1. 1School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel
  2. 2Sackler Institute of Molecular Medicine, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
  3. 3Basic Research Program, SAIC-Frederick, Inc., Center for Cancer Research Nanobiology Program NCI-Frederick, Frederick, Maryland 21702, USA

Abstract

There is a fast growing interest in noncoding RNA transcripts. These transcripts are not translated into proteins, but play essential roles in many cellular and pathological processes. Recent efforts toward comprehension of their function has led to a substantial increase in both the number and the size of solved RNA structures. With the aim of addressing questions relating to RNA structural diversity, we examined RNA conservation at three structural levels: primary, secondary, and tertiary structure. Additionally, we developed an automated method for classifying RNA structures based on spatial (three-dimensional [3D]) similarity. Applying the method to all solved RNA structures resulted in a classified database of RNA tertiary structures (DARTS). DARTS embodies 1333 solved RNA structures classified into 94 clusters. The classification is hierarchical, reflecting the structural relationship between and within clusters. We also developed an application for searching DARTS with a new structure. The search is fast and its performance was successfully tested on all solved RNA structures since the creation of DARTS. A user-friendly interface for both the database and the search application is available online. We show intracluster and intercluster similarities in DARTS and demonstrate the usefulness of the search application. The analysis reveals the current structural repertoire of RNA and exposes common global folds and local tertiary motifs. Further study of these conserved substructures may suggest possible RNA domains and building blocks. This should be beneficial for structure prediction and for gaining insights into structure–function relationships.

Keywords

Footnotes

  • Reprint requests to: Haim J. Wolfson, School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel; e-mail: wolfson{at}post.tau.ac.il; fax: 972-3-640 6476.

  • Article published online ahead of print. Article and publication date are at http://www.rnajournal.org/cgi/doi/10.1261/rna.853208.

  • 4 Data not shown, since a similar observation on a smaller representative data set is expressed in Figures 2A–C.

  • 5 The size ratio is calculated as the number of nucleotides of the bigger structure divided by the number of nucleotides of the smaller structure.

  • 6 We have disregarded the 2D identity score since for it the zero false-positive threshold is very high.

  • 7 The RNaseP RNA structure (PDB:2a2e) is annotated as RNaseP RNA catalytic domain. However, besides containing the catalytic domain in whole, the structure also contains some portions of the specificity domain. This enables the motif finding.

    • Received October 10, 2007.
    • Accepted July 5, 2008.

  • Freely available online through the open access option.

| Table of Contents
OPEN ACCESS ARTICLE

This Article

  1. Published in Advance September 29, 2008, doi: 10.1261/rna.853208 RNA 14: 2274-2289 Copyright © 2008 RNA Society

Article Category

Share

Current Issue

  1. May 2024, 30 (5)
  1. Current Issue
  1. Alert me to new issues of RNA