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

Abstract 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 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 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…


Link to Full Article: Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model