title: Predicting protein β-sheet contacts using a maximum entropy-based correlated mutation measure.
abstract: BACKGROUND
  The problem of ab initio protein folding is one of the most difficult in modern computational biology. The prediction of residue contacts within a protein provides a more tractable immediate step. Recently introduced maximum entropy-based correlated mutation measures (CMMs), such as direct information, have been successful in predicting residue contacts. However, most correlated mutation studies focus on proteins that have large good-quality multiple sequence alignments (MSA) because the power of correlated mutation analysis falls as the size of the MSA decreases. However, even with small autogenerated MSAs, maximum entropy-based CMMs contain information. To make use of this information, in this article, we focus not on general residue contacts but contacts between residues in β-sheets. The strong constraints and prior knowledge associated with β-contacts are ideally suited for prediction using a method that incorporates an often noisy CMM.
  
  
  RESULTS
  Using contrastive divergence, a statistical machine learning technique, we have calculated a maximum entropy-based CMM. We have integrated this measure with a new probabilistic model for β-contact prediction, which is used to predict both residue- and strand-level contacts. Using our model on a standard non-redundant dataset, we significantly outperform a 2D recurrent neural network architecture, achieving a 5% improvement in true positives at the 5% false-positive rate at the residue level. At the strand level, our approach is competitive with the state-of-the-art single methods achieving precision of 61.0% and recall of 55.4%, while not requiring residue solvent accessibility as an input.
  
  
  BACKGROUND
  http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software/
authors: Systems Biology Centre, Senate House, University of Warwick, Coventry, CV4 7AL, UK.
authors: Nikolas S
authors: NS
authors: Burkoff
authors: Csilla
authors: C
authors: Várnai
authors: David L
authors: DL
authors: Wild
mesh_terms: D001185:Artificial Intelligence; D019277:Entropy; D008958:Models, Molecular; D015233:Models, Statistical; D009154:Mutation; D016571:Neural Networks, Computer; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry / Q000235:genetics; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein
chemical_list: D011506:Proteins