title: A fast SCOP fold classification system using content-based E-Predict algorithm.
abstract: BACKGROUND
  Domain experts manually construct the Structural Classification of Protein (SCOP) database to categorize and compare protein structures. Even though using the SCOP database is believed to be more reliable than classification results from other methods, it is labor intensive. To mimic human classification processes, we develop an automatic SCOP fold classification system to assign possible known SCOP folds and recognize novel folds for newly-discovered proteins.
  
  
  RESULTS
  With a sufficient amount of ground truth data, our system is able to assign the known folds for newly-discovered proteins in the latest SCOP v1.69 release with 92.17% accuracy. Our system also recognizes the novel folds with 89.27% accuracy using 10 fold cross validation. The average response time for proteins with 500 and 1409 amino acids to complete the classification process is 4.1 and 17.4 seconds, respectively. By comparison with several structural alignment algorithms, our approach outperforms previous methods on both the classification accuracy and efficiency.
  
  
  CONCLUSIONS
  In this paper, we build an advanced, non-parametric classifier to accelerate the manual classification processes of SCOP. With satisfactory ground truth data from the SCOP database, our approach identifies relevant domain knowledge and yields reasonably accurate classifications. Our system is publicly accessible at http://ProteinDBS.rnet.missouri.edu/E-Predict.php.
authors.affiliation: Medical and Biological Digital Library Research Lab, Department of Computer Science, University of Missouri, Columbia, MO 65211, USA. pinhao@diglib1.cecs.missouri.edu
authors.forename: Pin-Hao
authors.initials: PH
authors.lastname: Chi
authors.forename: Chi-Ren
authors.initials: CR
authors.lastname: Shyu
authors.forename: Dong
authors.initials: D
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D030562:Databases, Protein; D008958:Models, Molecular; D010363:Pattern Recognition, Automated; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein; D012984:Software; D040681:Structural Homology, Protein; D018875:Vocabulary, Controlled
chemical_list: D011506:Proteins