  Despite considerable progress, ab initio protein structure prediction remains
suboptimal. A crowdsourcing approach is the online puzzle video game Foldit,
that provided several useful results that matched or even outperformed
algorithmically computed solutions. Using Foldit, the WeFold crowd had several
successful participations in the Critical Assessment of Techniques for Protein
Structure Prediction. Based on the recent Foldit standalone version, we trained
a deep reinforcement neural network called DeepFoldit to improve the score
assigned to an unfolded protein, using the Q-learning method with experience
replay. This paper is focused on model improvement through hyperparameter
tuning. We examined various implementations by examining different model
architectures and changing hyperparameter values to improve the accuracy of the
model. The new model hyper-parameters also improved its ability to generalize.
Initial results, from the latest implementation, show that given a set of small
unfolded training proteins, DeepFoldit learns action sequences that improve the
score both on the training set and on novel test proteins. Our approach
combines the intuitive user interface of Foldit with the efficiency of deep
reinforcement learning.
