Non-coding RNAs are known to play several roles in the cell, depending on their detailed structure. Their secondary structure can be predicted with reasonable accuracy by nearest-neighbor models based on thermodynamic parameters, as validated using analysis of covariant mutations. However, these models do not take into account tertiary contacts, and a different route must be followed for their prediction. Thanks to recent improvements in the sequencing technology, alignments of a large number of homologous sequences can now be obtained for each RNA family. It has been recently proposed to exploit the covariance in the mutations appearing in the alignments to predict tertiary contacts, using direct-coupling analysis within the mean field approximation , as successfully applied to proteins . We perform a similar analysis using a Boltzmann-learning procedure that overcomes some of the limitations of the mean-field approach and allows for a small but detectable improvement in the prediction of secondary and tertiary contacts when applied to a dataset including several riboswitches. Moreover, within this formulation, it is straightforward to include additional constraints coming from the knowledge about RNA-specific structural motifs.
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 Weinreb, C., Riesselman, A.J., Ingraham, J.B., Gross, T., Sander, C., and Marks, D.S. (2016). 3D RNA and Functional Interactions from Evolutionary Couplings. Cell 165,963-975.
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