title: Interaction profile-based protein classification of death domain.
abstract: BACKGROUND
  The increasing number of protein sequences and 3D structure obtained from genomic initiatives is leading many of us to focus on proteomics, and to dedicate our experimental and computational efforts on the creation and analysis of information derived from 3D structure. In particular, the high-throughput generation of protein-protein interaction data from a few organisms makes such an approach very important towards understanding the molecular recognition that make-up the entire protein-protein interaction network. Since the generation of sequences, and experimental protein-protein interactions increases faster than the 3D structure determination of protein complexes, there is tremendous interest in developing in silico methods that generate such structure for prediction and classification purposes. In this study we focused on classifying protein family members based on their protein-protein interaction distinctiveness. Structure-based classification of protein-protein interfaces has been described initially by Ponstingl et al. 1 and more recently by Valdar et al. 2 and Mintseris et al. 3, from complex structures that have been solved experimentally. However, little has been done on protein classification based on the prediction of protein-protein complexes obtained from homology modeling and docking simulation.
  
  
  RESULTS
  We have developed an in silico classification system entitled HODOCO (Homology modeling, Docking and Classification Oracle), in which protein Residue Potential Interaction Profiles (RPIPS) are used to summarize protein-protein interaction characteristics. This system applied to a dataset of 64 proteins of the death domain superfamily was used to classify each member into its proper subfamily. Two classification methods were attempted, heuristic and support vector machine learning. Both methods were tested with a 5-fold cross-validation. The heuristic approach yielded a 61% average accuracy, while the machine learning approach yielded an 89% average accuracy.
  
  
  CONCLUSIONS
  We have confirmed the reliability and potential value of classifying proteins via their predicted interactions. Our results are in the same range of accuracy as other studies that classify protein-protein interactions from 3D complex structure obtained experimentally. While our classification scheme does not take directly into account sequence information our results are in agreement with functional and sequence based classification of death domain family members.
authors.affiliation: Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, B,C, Canada, V5A 1S6. dclett@sfu.ca
authors.forename: Drew
authors.initials: D
authors.lastname: Lett
authors.forename: Michael
authors.initials: M
authors.lastname: Hsing
authors.forename: Frederic
authors.initials: F
authors.lastname: Pio
mesh_terms: D006801:Humans; D010455:Peptides / Q000145:classification / Q000502:physiology; D011237:Predictive Value of Tests; D025941:Protein Interaction Mapping / Q000145:classification*; D020836:Protein Structure, Quaternary; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D040901:Proteomics / Q000379:methods; D012984:Software
chemical_list: D010455:Peptides; D011506:Proteins