  RNA function crucially depends on its structure. Thermodynamic models
currently used for secondary structure prediction rely on computing the
partition function of folding ensembles, and can thus estimate minimum
free-energy structures and ensemble populations. These models sometimes fail in
identifying native structures unless complemented by auxiliary experimental
data. Here, we build a set of models that combine thermodynamic parameters,
chemical probing data (DMS, SHAPE), and co-evolutionary data (Direct Coupling
Analysis, DCA) through a network that outputs perturbations to the ensemble
free energy. Perturbations are trained to increase the ensemble populations of
a representative set of known native RNA structures. In the chemical probing
nodes of the network, a convolutional window combines neighboring reactivities,
enlightening their structural information content and the contribution of local
conformational ensembles. Regularization is used to limit overfitting and
improve transferability. The most transferable model is selected through a
cross-validation strategy that estimates the performance of models on systems
on which they are not trained. With the selected model we obtain increased
ensemble populations for native structures and more accurate predictions in an
independent validation set. The flexibility of the approach allows the model to
be easily retrained and adapted to incorporate arbitrary experimental
information.
