title: Deep architectures for protein contact map prediction.
abstract: BACKGROUND
  Residue-residue contact prediction is important for protein structure prediction and other applications. However, the accuracy of current contact predictors often barely exceeds 20% on long-range contacts, falling short of the level required for ab initio structure prediction.
  
  
  RESULTS
  Here, we develop a novel machine learning approach for contact map prediction using three steps of increasing resolution. First, we use 2D recursive neural networks to predict coarse contacts and orientations between secondary structure elements. Second, we use an energy-based method to align secondary structure elements and predict contact probabilities between residues in contacting alpha-helices or strands. Third, we use a deep neural network architecture to organize and progressively refine the prediction of contacts, integrating information over both space and time. We train the architecture on a large set of non-redundant proteins and test it on a large set of non-homologous domains, as well as on the set of protein domains used for contact prediction in the two most recent CASP8 and CASP9 experiments. For long-range contacts, the accuracy of the new CMAPpro predictor is close to 30%, a significant increase over existing approaches.
  
  
  BACKGROUND
  CMAPpro is available as part of the SCRATCH suite at http://scratch.proteomics.ics.uci.edu/.
  
  
  BACKGROUND
  pfbaldi@uci.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Department of Computer Science, University of California, Irvine, CA 92697, USA.
authors: Pietro
authors: P
authors: Di Lena
authors: Ken
authors: K
authors: Nagata
authors: Pierre
authors: P
authors: Baldi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins