title: Motif kernel generated by genetic programming improves remote homology and fold detection.
abstract: BACKGROUND
  Protein remote homology detection is a central problem in computational biology. Most recent methods train support vector machines to discriminate between related and unrelated sequences and these studies have introduced several types of kernels. One successful approach is to base a kernel on shared occurrences of discrete sequence motifs. Still, many protein sequences fail to be classified correctly for a lack of a suitable set of motifs for these sequences.
  
  
  RESULTS
  We introduce the GPkernel, which is a motif kernel based on discrete sequence motifs where the motifs are evolved using genetic programming. All proteins can be grouped according to evolutionary relations and structure, and the method uses this inherent structure to create groups of motifs that discriminate between different families of evolutionary origin. When tested on two SCOP benchmarks, the superfamily and fold recognition problems, the GPkernel gives significantly better results compared to related methods of remote homology detection.
  
  
  CONCLUSIONS
  The GPkernel gives particularly good results on the more difficult fold recognition problem compared to the other methods. This is mainly because the method creates motif sets that describe similarities among subgroups of both the related and unrelated proteins. This rich set of motifs give a better description of the similarities and differences between different folds than do previous motif-based methods.
authors.affiliation: Department of Computer and Information Science, Norwegian University of Science and Technology, NO-7052, Trondheim, Norway. tony.handstad@gmail.com
authors.forename: Tony
authors.initials: T
authors.lastname: Håndstad
authors.forename: Arne J H
authors.initials: AJ
authors.lastname: Hestnes
authors.forename: Pål
authors.initials: P
authors.lastname: Saetrom
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D010363:Pattern Recognition, Automated / Q000379:methods*; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012984:Software
chemical_list: D011506:Proteins