Fast and effective protein model refinement using deep graph neural networks

Abstract

Protein model refinement is the last step applied to improve the quality of a predicted protein model. Currently, the most successful refinement methods rely on extensive conformational sampling and thus take hours or days to refine even a single protein model. Here, we propose a fast and effective model refinement method that applies graph neural networks (GNNs) to predict a refined inter-atom distance probability distribution from an initial model and then rebuilds three-dimensional models from the predicted distance distribution. Tested on the Critical Assessment of Structure Prediction refinement targets, our method has an accuracy that is comparable to those of two leading human groups (FEIG and BAKER), but runs substantially faster. Our method may refine one protein model within ~11 min on one CPU, whereas BAKER needs ~30 h on 60 CPUs and FEIG needs ~16 h on one GPU. Finally, our study shows that GNN outperforms ResNet (convolutional residual neural networks) for model refinement when very limited conformational sampling is allowed.