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Protein Folding

Protein Folding

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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

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RNA folding with hard and soft constraints.

Ronny Lorenz¹, Ivo L Hofacker², Peter F Stadler³

¹Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, 1090 Vienna, Austria.

Algorithms for Molecular Biology : AMB

|April 26, 2016

Summary

The ViennaRNA Package now integrates external experimental data into RNA secondary structure prediction. This enhancement allows for more accurate modeling by incorporating diverse constraints with minimal computational cost.

Keywords:

Constraints Dynamic programming RNA folding

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Area of Science:

Computational Biology
Bioinformatics
Molecular Biology

Background:

RNA secondary structure prediction relies on complex energy models and dynamic programming.
Experimental data can guide predictions via hard or soft constraints.
Advances in probing techniques generate more data for RNA structure analysis.

Purpose of the Study:

To develop a flexible method for incorporating external evidence into RNA structure prediction.
To enhance the ViennaRNA Package with a generic constraint handling layer.

Main Methods:

Implemented a generic constraint handling layer within the ViennaRNA Package.
Separated the "folding grammar" (search space) from energy evaluation.
Interleaved constraints naturally within the prediction algorithm's recursion and energy steps.

Main Results:

The ViennaRNA Package now supports versatile inclusion of external evidence in RNA folding predictions.
The new framework seamlessly integrates diverse constraint types.
Computational overhead for constraint incorporation is negligible.

Conclusions:

The enhanced ViennaRNA Package offers a generic approach for integrating various constraints into RNA folding algorithms.
This framework accommodates diverse applications, including structure probing data, non-standard base pairs, chemical modifications, and ligand binding.
The integration of experimental data improves the accuracy and applicability of RNA secondary structure prediction.