title: HHfrag: HMM-based fragment detection using HHpred.
abstract: BACKGROUND
  Over the last decade, both static and dynamic fragment libraries for protein structure prediction have been introduced. The former are built from clusters in either sequence or structure space and aim to extract a universal structural alphabet. The latter are tailored for a particular query protein sequence and aim to provide local structural templates that need to be assembled in order to build the full-length structure.
  
  
  RESULTS
  Here, we introduce HHfrag, a dynamic HMM-based fragment search method built on the profile-profile comparison tool HHpred. We show that HHfrag provides advantages over existing fragment assignment methods in that it: (i) improves the precision of the fragments at the expense of a minor loss in sequence coverage; (ii) detects fragments of variable length (6-21 amino acid residues); (iii) allows for gapped fragments and (iv) does not assign fragments to regions where there is no clear sequence conservation. We illustrate the usefulness of fragments detected by HHfrag on targets from most recent CASP.
authors.affiliation: Department of Protein Evolution and Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany.
authors.forename: Ivan
authors.initials: I
authors.lastname: Kalev
authors.forename: Michael
authors.initials: M
authors.lastname: Habeck
mesh_terms: D008390:Markov Chains; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D011506:Proteins