title: Efficient similarity search in protein structure databases by k-clique hashing.
abstract: BACKGROUND
  Graph-based clique-detection techniques are widely used for the recognition of common substructures in proteins. They permit the detection of resemblances that are independent of sequence or fold homologies and are also able to handle conformational flexibility. Their high computational complexity is often a limiting factor and prevents a detailed and fine-grained modeling of the protein structure.
  
  
  RESULTS
  We present an efficient two-step method that significantly speeds up the detection of common substructures, especially when used to screen larger databases. It combines the advantages from both clique-detection and geometric hashing. The method is applied to an established approach for the comparison of protein binding-pockets, and some empirical results are presented.
  
  
  BACKGROUND
  Upon request from the authors.
authors.affiliation: Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Strasse, 35032 Marburg, Germany.
authors.forename: Nils
authors.initials: N
authors.lastname: Weskamp
authors.forename: Daniel
authors.initials: D
authors.lastname: Kuhn
authors.forename: Eyke
authors.initials: E
authors.lastname: Hüllermeier
authors.forename: Gerhard
authors.initials: G
authors.lastname: Klebe
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D044962:Data Compression / Q000379:methods*; D030562:Databases, Protein; D010363:Pattern Recognition, Automated / Q000379:methods*; D011485:Protein Binding; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*