  We present analysis of a novel tool for protein secondary structure
prediction using the recently-investigated Neural Machine Translation
framework. The tool provides a fast and accurate folding prediction based on
primary structure with subsecond prediction time even for batched inputs. We
hypothesize that Neural Machine Translation can improve upon current predictive
accuracy by better encoding complex relationships between nearby but
non-adjacent amino acids. We overview our modifications to the framework in
order to improve accuracy on protein sequences. We report 65.9% Q3 accuracy and
analyze the strengths and weaknesses of our predictive model.
