title: The distance-profile representation and its application to detection of distantly related protein families.
abstract: BACKGROUND
  Detecting homology between remotely related protein families is an important problem in computational biology since the biological properties of uncharacterized proteins can often be inferred from those of homologous proteins. Many existing approaches address this problem by measuring the similarity between proteins through sequence or structural alignment. However, these methods do not exploit collective aspects of the protein space and the computed scores are often noisy and frequently fail to recognize distantly related protein families.
  
  
  RESULTS
  We describe an algorithm that improves over the state of the art in homology detection by utilizing global information on the proximity of entities in the protein space. Our method relies on a vectorial representation of proteins and protein families and uses structure-specific association measures between proteins and template structures to form a high-dimensional feature vector for each query protein. These vectors are then processed and transformed to sparse feature vectors that are treated as statistical fingerprints of the query proteins. The new representation induces a new metric between proteins measured by the statistical difference between their corresponding probability distributions.
  
  
  CONCLUSIONS
  Using several performance measures we show that the new tool considerably improves the performance in recognizing distant homologies compared to existing approaches such as PSIBLAST and FUGUE.
authors.affiliation: Department of Computer Science, Cornell University, Ithaca, NY, USA. kucj@ece.cornell.edu
authors.forename: Chin-Jen
authors.initials: CJ
authors.lastname: Ku
authors.forename: Golan
authors.initials: G
authors.lastname: Yona
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D005822:Genetic Vectors; D006801:Humans; D015233:Models, Statistical; D005810:Multigene Family; D011336:Probability; D011506:Proteins / Q000737:chemistry; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D011506:Proteins