title: Improving structure alignment-based prediction of SCOP families using Vorolign kernels.
abstract: BACKGROUND
  The slow growth of expert-curated databases compared to experimental databases makes it necessary to build upon highly accurate automated processing pipelines to make the most of the data until curation becomes available. We address this problem in the context of protein structures and their classification into structural and functional classes, more specifically, the structural classification of proteins (SCOP). Structural alignment methods like Vorolign already provide good classification results, but effectively work in a 1-Nearest Neighbor mode. Model-based (in contrast to instance-based) approaches so far have been shown to be of limited values due to small classes arising in such classification schemes.
  
  
  RESULTS
  In this article, we describe how kernels defined in terms of Vorolign scores can be used in SVM learning, and explore variants of combined instance-based and model-based learning, up to exclusively model-based learning. Our results suggest that kernels based on Vorolign scores are effective and that model-based learning can yield highly competitive classification results for the prediction of SCOP families.
  
  
  BACKGROUND
  The code is made available at: http://wwwkramer.in.tum.de/research/applications/vorolign-kernel.
authors.affiliation: Institut für Informatik/I12, Technische Universität München, München, Germany.
authors.forename: Tobias
authors.initials: T
authors.lastname: Hamp
authors.forename: Fabian
authors.initials: F
authors.lastname: Birzele
authors.forename: Fabian
authors.initials: F
authors.lastname: Buchwald
authors.forename: Stefan
authors.initials: S
authors.lastname: Kramer
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002965:Classification / Q000379:methods; D008958:Models, Molecular; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry / Q000145:classification
chemical_list: D011506:Proteins