Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Protein contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method for contact prediction that predicts contacts by integrating both evolutionary coupling (EC) information and sequence conservation information through an ultra-deep neural network consisting of two deep residual neural networks. The two residual networks conduct a series of convolutional transformation of protein features including sequence profile, EC information and pairwise potential. This neural network allows us to model very complex relationship between sequence and contact map as well as long-range interdependency between contacts and thus, obtain high-quality contact prediction. Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. For example, on the 105 CASP11 test proteins, the L/10 long-range accuracy obtained by our method is 83.3% while that by CCMpred and MetaPSICOV (the CASP11 winner) is 43.4% and 60.2%, respectively. On the 398 membrane proteins, the L/10 long-range accuracy obtained by our method is 77.3% while that by CCMpred and MetaPSICOV is 51.8% and 61.2%, respectively. Ab initio folding guided by our predicted contacts can yield correct folds (i.e., TMscore>0.6) for 224 of the 579 test proteins, while that by MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Further, our contact-assisted models also have much better quality (especially for membrane proteins) than template-based models.