Study of real-valued distance prediction for protein structure prediction with deep learning

Motivation: Inter-residue distance prediction by convolutional residual neural network (deep ResNet) has greatly advanced protein structure prediction. Currently, the most successful structure prediction methods predict distance by discretizing it into dozens of bins. Here, we study how well real-valued distance can be predicted and how useful it is for 3D structure modeling by comparing it with discrete-valued prediction based upon the same deep ResNet. Results: Different from the recent methods that predict only a single real value for the distance of an atom pair, we predict both the mean and standard deviation of a distance and then fold a protein by the predicted mean and deviation. Our findings include: (i) tested on the CASP13 FM (free-modeling) targets, our real-valued distance prediction obtains 81% precision on top L/5 long-range contact prediction, much better than the best CASP13 results (70%); our real-valued prediction can predict correct folds for the same number of CASP13 FM targets as the best CASP13 group, despite generating only 20 decoys for each target; (iii) our method greatly outperforms a very new real-valued prediction method DeepDist in both contact prediction and 3D structure modeling and (iv) when the same deep ResNet is used, our real-valued distance prediction has 1-6% higher contact and distance accuracy than our own discrete-valued prediction, but less accurate 3D structure models.