Deep learning RNA sequences Currently I'm working on a project, which combines deep learning with RNA sequences. I'll try to predict pseudotorsion angles [1] from raw rna sequence. The ideas is to train a neural network with raw rna sequences and for each nucleotide their corresponding pseudotorsion angles, and than to predict the angles of the remaining sequences in the test set.

How my data is structured:
example:

seq1:   A  C  G    G   U   A   C
Eta:   169 87 110 87  45  187 78
Theta: 123 10 45  168 132 34  100 


[1] These are angles describing the backbone conformation of a rna molecule.

I'm pretty new to the field of deep learning, and so far I build a simple feedforward neural network, but it's prediction accuracy is pretty low with only one percent. 

Has anyone some tips for me how to improve this? 
How do I preprocess this kind of data correctly for deep learning?

I appreciate any help.
The question of folding of RNA sequences looks slightly similar to protein folding - perhaps searching in this domain might bring more suggestions. 

An example of (current) state of the art of deep learning approach to protein folding is AlphaFold developed by DeepMind for CASP competition . They basically decompose the angles to an image representation and look for structures in the proximity of each atom. While it requires a significant processing power, RNA structures might be easier due to the size of the sequence.