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Machine learning and the use of neural networks has increased precipitouslyover the past few years primarily due to the ever-increasing accessibility todata and the growth of computation power. It has become increasingly easy toharness the power of machine learning for predictive tasks. Protein structureprediction is one area where neural networks are becoming increasingly popularand successful. Although very powerful, the use of ANN require selection ofmost appropriate input/output encoding, architecture, and class to produce theoptimal results. In this investigation we have explored and evaluated theeffect of several conventional and newly proposed input encodings and selectedan optimal architecture. We considered 11 variations of input encoding, 11alternative window sizes, and 7 different architectures. In total, we evaluated2,541 permutations in application to the training and testing of more than10,000 protein structures over the course of 3 months. Our investigationsconcluded 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 toimprove the quality of protein structure prediction by predicting the {\phi}dihedrals to within 14{\deg} - 16{\deg} and {\psi} dihedrals to within23{\deg}- 25{\deg}. This is a notable improvement compared to previouslysimilar investigations.

An Investigation in Optimal Encoding of Protein Primary Sequence for Structure Prediction by Artificial Neural Networks