@article{discovery1460206,
            note = {This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. 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.},
           month = {November},
            year = {2014},
           title = {MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins.},
         journal = {Bioinformatics},
            issn = {1367-4803},
             url = {http://dx.doi.org/10.1093/bioinformatics/btu791},
          author = {Jones, DT and Singh, T and Kosciolek, T and Tetchner, S},
        abstract = {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.}
}