title: Clustering protein sequences--structure prediction by transitive homology.
abstract: BACKGROUND
  It is widely believed that for two proteins Aand Ba sequence identity above some threshold implies structural similarity due to a common evolutionary ancestor. Since this is only a sufficient, but not a necessary condition for structural similarity, the question remains what other criteria can be used to identify remote homologues. Transitivity refers to the concept of deducing a structural similarity between proteins A and C from the existence of a third protein B, such that A and B as well as B and C are homologues, as ascertained if the sequence identity between A and B as well as that between B and C is above the aforementioned threshold. It is not fully understood if transitivity always holds and whether transitivity can be extended ad infinitum.
  
  
  RESULTS
  We developed a graph-based clustering approach, where transitivity plays a crucial role. We determined all pair-wise similarities for the sequences in the SwissProt database using the Smith-Waterman local alignment algorithm. This data was transformed into a directed graph, where protein sequences constitute vertices. A directed edge was drawn from vertex A to vertex B if the sequences A and B showed similarity, scaled with respect to the self-similarity of A, above a fixed threshold. Transitivity was important in the clustering process, as intermediate sequences were used, limited though by the requirement of having directed paths in both directions between proteins linked over such sequences. The length dependency-implied by the self-similarity-of the scaling of the alignment scores appears to be an effective criterion to avoid clustering errors due to multi-domain proteins. To deal with the resulting large graphs we have developed an efficient library. Methods include the novel graph-based clustering algorithm capable of handling multi-domain proteins and cluster comparison algorithms. Structural Classification of Proteins (SCOP) was used as an evaluation data set for our method, yielding a 24% improvement over pair-wise comparisons in terms of detecting remote homologues.
  
  
  BACKGROUND
  The software is available to academic users on request from the authors.
  
  
  BACKGROUND
  e.bolten@science-factory.com; schliep@zpr.uni-koeln.de; s.schneckener@science-factory.com; d.schomburg@uni-koeln.de; schrader@zpr.uni-koeln.de.
  
  
  BACKGROUND
  http://www.zaik.uni-koeln.de/~schliep/ProtClust.html.
authors.affiliation: Institut für Biochemie, Universität zu Köln, Weyertal 80, D-50937 Köln, Germany. e.bolten@science-factory.com
authors.forename: E
authors.initials: E
authors.lastname: Bolten
authors.forename: A
authors.initials: A
authors.lastname: Schliep
authors.forename: S
authors.initials: S
authors.lastname: Schneckener
authors.forename: D
authors.initials: D
authors.lastname: Schomburg
authors.forename: R
authors.initials: R
authors.lastname: Schrader
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D030562:Databases, Protein / Q000706:statistics & numerical data; D011506:Proteins / Q000235:genetics*; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000706:statistics & numerical data*; D017386:Sequence Homology, Amino Acid; D012984:Software
chemical_list: D011506:Proteins