title: MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins.
abstract: BACKGROUND
  Recent developments of statistical techniques to infer direct evolutionary couplings between residue pairs have rendered covariation-based contact prediction a viable means for accurate 3D modelling of proteins, with no information other than the sequence required. To extend the usefulness of contact prediction, we have designed a new meta-predictor (MetaPSICOV) which combines three distinct approaches for inferring covariation signals from multiple sequence alignments, considers a broad range of other sequence-derived features and, uniquely, a range of metrics which describe both the local and global quality of the input multiple sequence alignment. Finally, we use a two-stage predictor, where the second stage filters the output of the first stage. This two-stage predictor is additionally evaluated on its ability to accurately predict the long range network of hydrogen bonds, including correctly assigning the donor and acceptor residues.
  
  
  RESULTS
  Using the original PSICOV benchmark set of 150 protein families, MetaPSICOV achieves a mean precision of 0.54 for top-L predicted long range contacts-around 60% higher than PSICOV, and around 40% better than CCMpred. In de novo protein structure prediction using FRAGFOLD, MetaPSICOV is able to improve the TM-scores of models by a median of 0.05 compared with PSICOV. Lastly, for predicting long range hydrogen bonding, MetaPSICOV-HB achieves a precision of 0.69 for the top-L/10 hydrogen bonds compared with just 0.26 for the baseline MetaPSICOV.
  
  
  METHODS
  MetaPSICOV is available as a freely available web server at http://bioinf.cs.ucl.ac.uk/MetaPSICOV. Raw data (predicted contact lists and 3D models) and source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Bioinformatics Group, Department of Computer Science, University College London, London WC1E 6BT, UK.
authors.forename: David T
authors.initials: DT
authors.lastname: Jones
authors.affiliation: Bioinformatics Group, Department of Computer Science, University College London, London WC1E 6BT, UK.
authors.forename: Tanya
authors.initials: T
authors.lastname: Singh
authors.affiliation: Bioinformatics Group, Department of Computer Science, University College London, London WC1E 6BT, UK.
authors.forename: Tomasz
authors.initials: T
authors.lastname: Kosciolek
authors.affiliation: Bioinformatics Group, Department of Computer Science, University College London, London WC1E 6BT, UK.
authors.forename: Stuart
authors.initials: S
authors.lastname: Tetchner
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D006860:Hydrogen Bonding; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D011506:Proteins