1,760 research outputs found
Modeling RNA loops using sequence homology and geometric constraints
Summary: RNA loop regions are essential structural elements of RNA molecules influencing both their structural and functional properties. We developed RLooM, a web application for homology-based modeling of RNA loops utilizing template structures extracted from the PDB. RLooM allows the insertion and replacement of loop structures of a desired sequence into an existing RNA structure. Furthermore, a comprehensive database of loops in RNA structures can be accessed through the web interface
Ab initio RNA folding
RNA molecules are essential cellular machines performing a wide variety of
functions for which a specific three-dimensional structure is required. Over
the last several years, experimental determination of RNA structures through
X-ray crystallography and NMR seems to have reached a plateau in the number of
structures resolved each year, but as more and more RNA sequences are being
discovered, need for structure prediction tools to complement experimental data
is strong. Theoretical approaches to RNA folding have been developed since the
late nineties when the first algorithms for secondary structure prediction
appeared. Over the last 10 years a number of prediction methods for 3D
structures have been developed, first based on bioinformatics and data-mining,
and more recently based on a coarse-grained physical representation of the
systems. In this review we are going to present the challenges of RNA structure
prediction and the main ideas behind bioinformatic approaches and physics-based
approaches. We will focus on the description of the more recent physics-based
phenomenological models and on how they are built to include the specificity of
the interactions of RNA bases, whose role is critical in folding. Through
examples from different models, we will point out the strengths of
physics-based approaches, which are able not only to predict equilibrium
structures, but also to investigate dynamical and thermodynamical behavior, and
the open challenges to include more key interactions ruling RNA folding.Comment: 28 pages, 18 figure
3D modeling of ribosomal RNA using cryo-electron microscopy density maps
Ribosomes are macromolecular protein-RNA complexes translating mRNA into protein. To date,
crystal structures are available for the bacterial 30S and archaeal 50S subunits, as well as the
complete bacterial 70S ribosomes. Eukaryotic ribosomes are much more complex in terms of
ribosomal RNA and proteins. However, to date high-resolution crystal structures of eukaryotic
ribosomes or ribosomal subunits are lacking.
In order to build reliable models for the eukaryotic rRNA, we developed an approach for large
scale homology and de novo modeling of RNA and subsequent exible tting into high-resolution
cryo-EM density maps.
Using this approach we built a model of the T. aestivum and the S. cerevisiae ribosome based
on available cryo-EM maps at 5.5 Å and 6.1 Å resolution, respectively. The model comprises of
98% of the eukaryotic rRNA including all 21 RNA expansion segments (ES) and structurally
six variable regions. Further, we were able to localize 74/80 (92.5%) of the ribosomal proteins.
The model reveals unique ES-ES and r-protein-ES interactions, providing new insight into the
structure and evolution of the eukaryotic ribosome. Moreover, the model was used for analyzing
functional ribosomal complexes, i.e. the characterization of dierent nascent polypeptide chains
within the ribosomal tunnel, intermediates of protein translocation as well as mRNA quality
control
Accelerated probabilistic inference of RNA structure evolution
BACKGROUND: Pairwise stochastic context-free grammars (Pair SCFGs) are powerful tools for evolutionary analysis of RNA, including simultaneous RNA sequence alignment and secondary structure prediction, but the associated algorithms are intensive in both CPU and memory usage. The same problem is faced by other RNA alignment-and-folding algorithms based on Sankoff's 1985 algorithm. It is therefore desirable to constrain such algorithms, by pre-processing the sequences and using this first pass to limit the range of structures and/or alignments that can be considered. RESULTS: We demonstrate how flexible classes of constraint can be imposed, greatly reducing the computational costs while maintaining a high quality of structural homology prediction. Any score-attributed context-free grammar (e.g. energy-based scoring schemes, or conditionally normalized Pair SCFGs) is amenable to this treatment. It is now possible to combine independent structural and alignment constraints of unprecedented general flexibility in Pair SCFG alignment algorithms. We outline several applications to the bioinformatics of RNA sequence and structure, including Waterman-Eggert N-best alignments and progressive multiple alignment. We evaluate the performance of the algorithm on test examples from the RFAM database. CONCLUSION: A program, Stemloc, that implements these algorithms for efficient RNA sequence alignment and structure prediction is available under the GNU General Public License
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