DeepDist: real-value inter-residue distance prediction with deep residual network

Abstract

MotivationDriven by deep learning techniques, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. Currently all the distance prediction methods classify inter-residue distances into multiple distance intervals (i.e. a multi-classification problem) instead of directly predicting real-value distances (i.e. a regression problem). The output of the former has to be converted into real-value distances in order to be used in tertiary structure prediction. ResultsTo explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. We demonstrate that predicting the real-value distance map and multi-class distance map at the same time performs better than predicting real-value distances alone, indicating their complementarity. On 43 CASP13 hard domains, the average mean square error (MSE) of DeepDists real-value distance predictions is 0.896 [A] when filtering out the predicted distance >=16 [A], which is lower than 1.003 [A] of DeepDists multi-class distance predictions. When the predicted real-value distances are converted to binary contact predictions at 8[A] threshold, the precisions of top L/5 and L/2 contact predictions are 78.6% and 64.5%, respectively, higher than the best results reported in the CASP13 experiment. These results demonstrate that the real-value distance prediction can predict inter-residue distances well and improve binary contact prediction over the existing state-of-the-art methods. Moreover, the predicted real-value distances can be directly used to reconstruct protein tertiary structures better than multi-class distance predictions due to the lower MSE.