  Machine learning and the use of neural networks has increased precipitously
over the past few years primarily due to the ever-increasing accessibility to
data and the growth of computation power. It has become increasingly easy to
harness the power of machine learning for predictive tasks. Protein structure
prediction is one area where neural networks are becoming increasingly popular
and successful. Although very powerful, the use of ANN require selection of
most appropriate input/output encoding, architecture, and class to produce the
optimal results. In this investigation we have explored and evaluated the
effect of several conventional and newly proposed input encodings and selected
an optimal architecture. We considered 11 variations of input encoding, 11
alternative window sizes, and 7 different architectures. In total, we evaluated
2,541 permutations in application to the training and testing of more than
10,000 protein structures over the course of 3 months. Our investigations
concluded that one-hot encoding, the use of LSTMs, and window sizes of 9, 11,
and 15 produce the optimal outcome. Through this optimization, we were able to
improve the quality of protein structure prediction by predicting the {\phi}
dihedrals to within 14{\deg} - 16{\deg} and {\psi} dihedrals to within
23{\deg}- 25{\deg}. This is a notable improvement compared to previously
similar investigations.
