title: Remote homology detection: a motif based approach.
abstract: BACKGROUND
  Remote homology detection is the problem of detecting homology in cases of low sequence similarity. It is a hard computational problem with no approach that works well in all cases.
  
  
  RESULTS
  We present a method for detecting remote homology that is based on the presence of discrete sequence motifs. The motif content of a pair of sequences is used to define a similarity that is used as a kernel for a Support Vector Machine (SVM) classifier. We test the method on two remote homology detection tasks: prediction of a previously unseen SCOP family and prediction of an enzyme class given other enzymes that have a similar function on other substrates. We find that it performs significantly better than an SVM method that uses BLAST or Smith-Waterman similarity scores as features.
authors.affiliation: Department of Biochemistry, B400 Beckman Center, Stanford University, CA 94305-5307, USA. asa.benhur@stanford.edu
authors.forename: Asa
authors.initials: A
authors.lastname: Ben-Hur
authors.forename: Douglas
authors.initials: D
authors.lastname: Brutlag
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003205:Computing Methodologies; D030561:Databases, Nucleic Acid; D004798:Enzymes / Q000737:chemistry / Q000145:classification; D009687:Nuclear Proteins / Q000737:chemistry / Q000145:classification; D010363:Pattern Recognition, Automated; D010749:Phosphoprotein Phosphatases; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012984:Software
chemical_list: D004798:Enzymes; D009687:Nuclear Proteins; D011506:Proteins; C400601:PHLPP1 protein, human; D010749:Phosphoprotein Phosphatases