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

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 very 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…

• NS
  National Science Foundation

  Awards: DBI-1564955, 1564955
• N
  Nvidia
• NI
  National Institutes of Health

  Award: R01GM089753
• DF
  Directorate for Biological Sciences

  Award: DBI-1564955
• NI
  National Institute of General Medical Sciences

  Award: R01GM089753