Maximal helices weighted by experimental probing data to infer RNA topology*

Abstract: Non-coding RNAs are biological molecules known for their uncountable roles as transcription regulators (rRNA), carriers of free amino acid (tRNA), etc. Functions that are only performed if the RNA folds into a specific topology. The secondary structure is a representation of this topology. This structure can be broken down into small functional units called helices, which are in turn the result of stacked base pair interactions. Several challenges to predict RNA structure may be encountered at the algorithmic level as well as at the energy parametrization model level; Besides the complexity of the RNA structure and its ability to alternate between multiple topologies, most prediction models lack pseudo-knots, a vital aspect of biological activity. Recent methods that combine in-silico modeling methods with experimental probing data have shown to lead to more accurate predictions but are not fully capable of capturing pseudo-knot interactions. Our method RedMaxH, described as a free free-energy model allowing to weight structural units (maximal helices) with mutational probing data, has shown its ability to decipher these RNA complex interactions. In this talk, we will start by providing an overview of the difficulties in predicting RNA topology. We will also show how the use of probing data, more specifically mutational data, with the RedMaxH method allows to infer pseudo-knots for certain RNAs. Next, we will share how this method can be used to compare conformational changes in the presence of data generated in different experimental setups. Finally, we will discuss ongoing work on linking maximal helices, that are supported by experimental evidence, to predict the overall structural topology of RNA.

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