Currently I'm working on a project, which combines deep learning with RNA sequences. I'll try to predict pseudotorsion angles  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
 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.