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      PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments.

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          Abstract

          The accurate prediction of residue-residue contacts, critical for maintaining the native fold of a protein, remains an open problem in the field of structural bioinformatics. Interest in this long-standing problem has increased recently with algorithmic improvements and the rapid growth in the sizes of sequence families. Progress could have major impacts in both structure and function prediction to name but two benefits. Sequence-based contact predictions are usually made by identifying correlated mutations within multiple sequence alignments (MSAs), most commonly through the information-theoretic approach of calculating mutual information between pairs of sites in proteins. These predictions are often inaccurate because the true covariation signal in the MSA is often masked by biases from many ancillary indirect-coupling or phylogenetic effects. Here we present a novel method, PSICOV, which introduces the use of sparse inverse covariance estimation to the problem of protein contact prediction. Our method builds on work which had previously demonstrated corrections for phylogenetic and entropic correlation noise and allows accurate discrimination of direct from indirectly coupled mutation correlations in the MSA.

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          Author and article information

          Journal
          Bioinformatics
          Bioinformatics (Oxford, England)
          Oxford University Press (OUP)
          1367-4811
          1367-4803
          Jan 15 2012
          : 28
          : 2
          Affiliations
          [1 ] Department of Computer Science, Bioinformatics Group, Centre for Computational Statistics and Machine Learning, University College London, Malet Place, London WC1E 6BT, UK. d.jones@cs.ucl.ac.uk
          Article
          btr638
          10.1093/bioinformatics/btr638
          22101153
          671a0daf-5e6a-4dbe-8118-3bbe0568f9ce
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