Abstract
Thermodynamic folding algorithms and structure probing experiments are commonly used to determine the secondary structure of RNAs. Here we
propose a formal framework to reconcile information from both prediction algorithms and probing experiments. The thermodynamic energy parameters are adjusted using ‘pseudo-energies’ to minimize the discrepancy between prediction and experiment. Our framework differs from related approaches that used pseudo-energies in several key aspects. (i) The energy model is only changed when necessary and no adjustments are made if
prediction and experiment are consistent. (ii) Pseudo-energies remain biophysically interpretable and hold positional information where experiment
and model disagree. (iii) The whole thermodynamic ensemble of structures is considered thus allowing to reconstruct mixtures of suboptimal structures
from seemingly contradicting data. (iv) The noise of the energy model and the experimental data is explicitly modeled leading to an intuitive weighting
factor through which the problem can be seen as folding with ‘soft’ constraints of different strength.
We present an efficient algorithm to iteratively calculate pseudo-energies within this framework and demonstrate how this approach can be used in
combination with SHAPE chemical probing data to improve secondary structure prediction. We further demonstrate that the pseudo-energies correlate with biophysical effects that are known to affect
RNA folding such as chemical nucleotide
modifications and protein binding.