DeepFoldit -- A Deep Reinforcement Learning Neural Network Folding Proteins

TL;DR

DeepFoldit leverages deep reinforcement learning to enhance protein folding predictions by training a neural network that learns to improve scores on unfolded proteins, combining user interface insights with algorithmic efficiency.

Contribution

This work introduces DeepFoldit, a deep reinforcement learning model trained on Foldit data, to improve ab initio protein structure prediction accuracy.

Findings

DeepFoldit learns action sequences that improve scores on training proteins.

The model generalizes well to novel test proteins.

Hyperparameter tuning enhances model performance and generalization.

Abstract

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.