title: Context similarity scoring improves protein sequence alignments in the midnight zone.
abstract: BACKGROUND
  High-quality protein sequence alignments are essential for a number of downstream applications such as template-based protein structure prediction. In addition to the similarity score between sequence profile columns, many current profile-profile alignment tools use extra terms that compare 1D-structural properties such as secondary structure and solvent accessibility, which are predicted from short profile windows around each sequence position. Such scores add non-redundant information by evaluating the conservation of local patterns of hydrophobicity and other amino acid properties and thus exploiting correlations between profile columns.
  
  
  RESULTS
  Here, instead of predicting and comparing known 1D properties, we follow an agnostic approach. We learn in an unsupervised fashion a set of maximally conserved patterns represented by 13-residue sequence profiles, without the need to know the cause of the conservation of these patterns. We use a maximum likelihood approach to train a set of 32 such profiles that can best represent patterns conserved within pairs of remotely homologs, structurally aligned training profiles. We include the new context score into our Hmm-Hmm alignment tool hhsearch and improve especially the quality of difficult alignments significantly.
  
  
  CONCLUSIONS
  The context similarity score improves the quality of homology models and other methods that depend on accurate pairwise alignments.
authors.affiliation: Gene Center, LMU Munich, 81377 Munich and Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany.
authors.forename: Armin
authors.initials: A
authors.lastname: Meier
authors.affiliation: Gene Center, LMU Munich, 81377 Munich and Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany Gene Center, LMU Munich, 81377 Munich and Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany.
authors.forename: Johannes
authors.initials: J
authors.lastname: Söding
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D016013:Likelihood Functions; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D000596:Amino Acids; D011506:Proteins