TY - JOUR T1 - Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model JF - bioRxiv DO - 10.1101/073239 SP - 073239 AU - Wang Sheng AU - Siqi Sun AU - Zhen Li AU - Renyu Zhang AU - Jinbo Xu Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/09/16/073239.abstract N2 - Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, 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 extremely useful for de novo structure prediction.Method This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can model very complex relationship between sequence and contact map as well as long-range interdependency between contacts and thus, obtain high-quality contact prediction.Results Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. Tested on the 105 CASP11 targets, 76 CAMEO test proteins and 398 membrane proteins, the average top L long-range prediction accuracy obtained our method, the representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using 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 than template-based models (especially for membrane proteins). Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast, when the training proteins of our method are used as templates, homology modeling can only do so for 10 of them. One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein contact prediction. In the recent blind CAMEO benchmark, our method successfully folded one mainly-beta protein of 182 residues with a novel fold.Availability http://raptorx.uchicago.edu/ContactMap/Author Summary Protein contact prediction from sequence alone is an important problem. Recently exciting progress has been made on this problem due to the development of direct evolutionary coupling (EC). However, direct EC analysis methods are effective on only some proteins with a very large number (>1000) of sequence homologs. To further improve contact prediction, we borrow ideas from the latest breakthrough of deep learning. Deep learning is a powerful machine learning technique and has recently revolutionized object recognition, speech recognition and the GO game. We have developed a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network, which can model very complex relationship between sequence and contact map as well as long-range interdependency between contacts.Our test results suggest that deep learning can also revolutionize protein contact prediction. Tested on 398 membrane proteins, the L/10 long-range accuracy obtained by our method is 77.6% while that by the state-of-the-art methods CCMpred and MetaPSICOV is 51.8% and 61.2%, respectively. Ab initio folding using our predicted contacts as restraints can generate much better 3D structural models than the other contact prediction methods. In particular, our predicted contacts yield correct folds for 203 of the 579 test proteins, while MetaPSICOV- and CCMpred can do so for only 79 and 62 of them, respectively. Finally, our contact-assisted models also have much better quality than template-based models (TBM) built from the training proteins. For example, our contact-assisted models have TMscore>0.5 for 208 of the 398 membrane proteins while the TBMs have TMscore >0.5 for only 10 of them. We also find out that even without using any membrane proteins to train our deep learning models, our method still performs very well on membrane protein contact prediction. Recent blind test of our method in CAMEO shows that our method successfully folded a mainly-beta protein of 182 residues. ER -