PSI Structural Biology Knowledgebase

PSI | Structural Biology Knowledgebase
Header Icons

Related Articles
Signaling: A Platform for Opposing Functions
May 2015
Protein Folding and Misfolding: It's the Journey, Not the Destination
March 2015
Molecular Portraits of the Cell
February 2015
Nuclear Pore Complex: A Flexible Transporter
February 2015
Nuclear Pore Complex: Higher Resolution of Macromolecules
February 2015
Nuclear Pore Complex: Integrative Approach to Probe Nup133
February 2015
Piecing Together the Nuclear Pore Complex
February 2015
Updating ModBase
January 2015
Transmembrane Spans
December 2014
Mining Protein Dynamics
May 2014
Novel Proteins and Networks: Assigning Function
May 2014
Cancer Networks: Predicting Catalytic Residues from 3D Protein Structures
November 2013
The Immune System: A Brotherhood of Immunoglobulins
June 2013
The Immune System: Super Cytokines
June 2013
Infectious Diseases: Targeting Meningitis
May 2013
PDZ Domains
April 2013
Protein Interaction Networks: Adding Structure to Protein Networks
April 2013
Design and Discovery: Flexible Backbone Protein Redesign
February 2013
Pocket changes
July 2012
Predictive protein origami
July 2012
Refining protein structure prediction
March 2012
Metal mates
February 2012
Devil is in the details
January 2012
Playing while you work
November 2011
Docking and rolling
October 2011
Fit to serve
October 2011
Rosetta hone
July 2011
Structure from sequence
July 2011
An easier solution for symmetry
June 2011
Solutions in the solution
June 2011
Regulating nitrogen assimilation
January 2011
Guard cells pick up the SLAC
December 2010
Alpha/Beta Barrels
October 2010
Modeling RNA structures
May 2010
Deducing function from small structural clues
February 2010
Spot the pore
January 2010
Network coverage
November 2009
GPCR modeling: any good?
August 2009
Protein modeling made easy
July 2009
Model proteins in your lunch break
April 2009
Click for cancer-protein interactions
December 2008
Modeling with SAXS
October 2008
Designing activity
September 2008

Technology Topics Modeling

Structure from sequence

SBKB [doi:10.1038/sbkb.2011.29]
Technical Highlight - July 2011
Short description: A new computational tool will allow researchers to easily predict RNA structural modules from the sequence alone.

RMDetect uses sequence information to predict the presence of RNA structural modules. Reprinted from Nature Methods. 1

It is no secret that tertiary structures in RNA are almost, if not equally, as important as the RNA sequence itself. These structures are formed by short- and long-range interactions between the bases in the RNA and often take the form of specific motifs, also referred to as modules. These modules include G-bulges, kink-turns, C-loops and tandem GA/AG loops (tandem GAs). However, although there are tools to predict RNA secondary structure motifs, they are limited by their ineffectiveness to treat the whole range of sequence variations. Three-dimensional structure prediction tools, which can also help to identify structural modules, are often not user-friendly in terms of the expertise and computer time they require to be used effectively.

Now, Cruz and Westhof have generated a new computational tool for detecting RNA structural modules on the basis of the RNA sequence alone. They name their tool RMDetect, short for RNA three-dimensional modules detection. Using Bayesian network models, base-pair probability prediction and positional clustering of candidates, RMDetect can predict the presence of each of the above-mentioned structural modules.

To validate the utility of RMDetect, the authors tested it against single target sequences as well as multiple sequence alignments. RMDetect performed well on both, although the false discovery rate (FDR) for tandem GAs in the single sequence analysis was higher than for the other modules. To counteract this, the authors caution that the training data set used for each model needs to be as complete and representative of the population as possible. RMDetect performed better on multiple sequence alignments (even when the same data sets tested in the single sequence analysis were used), demonstrating that the additional information provided by the alignments added to the robustness of the tool.

When RMDetect was applied to sequence alignments from the available databases and published data (including bacterial data), the authors uncovered several new structural modules of each type tested (G-bulges, kink-turns, C-loops and tandem GAs), with a particular prevalence of tandem GAs in some cases. This demonstrates that the RMDetect is a robust tool that will be integral to the hunt for tertiary RNA structural elements. The authors have also made available RMBuild, a tool to allow additional modules to be added to RMDetect, which will undoubtedly increase its utility and appeal to other researchers. This will then pave the way for the detection of RNA structural modules in many species, allowing greater insight into their biological significance.

Steve Mason


  1. J.A. Cruz, E. Westhof Sequence-based identification of 3D structural modules in RNA with RMDetect.
    Nat Methods 8, 513-519 (2011). doi:10.1038/nmeth.1603

Structural Biology Knowledgebase ISSN: 1758-1338
Funded by a grant from the National Institute of General Medical Sciences of the National Institutes of Health