title: Rapid similarity search of proteins using alignments of domain arrangements.
abstract: BACKGROUND
  Homology search methods are dominated by the central paradigm that sequence similarity is a proxy for common ancestry and, by extension, functional similarity. For determining sequence similarity in proteins, most widely used methods use models of sequence evolution and compare amino-acid strings in search for conserved linear stretches. Probabilistic models or sequence profiles capture the position-specific variation in an alignment of homologous sequences and can identify conserved motifs or domains. While profile-based search methods are generally more accurate than simple sequence comparison methods, they tend to be computationally more demanding. In recent years, several methods have emerged that perform protein similarity searches based on domain composition. However, few methods have considered the linear arrangements of domains when conducting similarity searches, despite strong evidence that domain order can harbour considerable functional and evolutionary signal.
  
  
  RESULTS
  Here, we introduce an alignment scheme that uses a classical dynamic programming approach to the global alignment of domains. We illustrate that representing proteins as strings of domains (domain arrangements) and comparing these strings globally allows for a both fast and sensitive homology search. Further, we demonstrate that the presented methods complement existing methods by finding similar proteins missed by popular amino-acid-based comparison methods.
  
  
  BACKGROUND
  An implementation of the presented algorithms, a web-based interface as well as a command-line program for batch searching against the UniProt database can be found at http://rads.uni-muenster.de. Furthermore, we provide a JAVA API for programmatic access to domain-string–based search methods.
authors.affiliation: Westfalian Wilhelms University, Institute of Evolution and Biodiversity, Huefferstr. 1, 48149 Muenster, Germany and Max Planck Institute for Infection Biology, Charitéplatz 1, 10117 Berlin, Germany.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Terrapon
authors.forename: January
authors.initials: J
authors.lastname: Weiner
authors.forename: Sonja
authors.initials: S
authors.lastname: Grath
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Moore
authors.forename: Erich
authors.initials: E
authors.lastname: Bornberg-Bauer
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D020690:GTPase-Activating Proteins / Q000737:chemistry; D015233:Models, Statistical; D017875:Phosphotransferases (Phosphomutases) / Q000737:chemistry; D054730:Protein Interaction Domains and Motifs; D011506:Proteins / Q000737:chemistry*; D012372:ROC Curve; D012441:Saccharomyces cerevisiae; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D020690:GTPase-Activating Proteins; D011506:Proteins; D017875:Phosphotransferases (Phosphomutases); C052799:phosphomannomutase