title: The properties of protein family space depend on experimental design.
abstract: BACKGROUND
  Databases of protein families often exhibit drastically different properties of the protein family space.
  
  
  RESULTS
  We compared the properties of protein family space as reflected by exhaustive protein family databases and databases with predefined families. We used TRIBES, Protomap, ProDom and COGs as representatives of the exhaustive databases, and Pfam-A and Superfamily as databases that predefine families. We observe a power-law distribution of family sizes in all these databases, albeit in predefined databases the power-law line collapses before reaching smaller sized families. We discuss the future trends of this power-law distribution and suggest that saturation in the sampling of protein family space will result in a distortion of the power law in small family sizes. For larger genome sizes, predefined databases show logarithmic growth of the number of families per genome, whereas exhaustive databases exhibit a virtually linear relationship. All databases consistently differ in the proportion of protein families shared between taxa. Predefined databases have a larger number of protein families shared between the three domains of life, while exhaustive databases show a much more fragmented distribution. We argue that these discrepancies reflect alternative approaches to the trade-off issue of sensitivity versus specificity in the detection of homologous proteins. We conclude that these properties are complementary rather than contradictory, while describing the protein universe from different perspectives.
authors.affiliation: Computational Genomics Group, The European Bioinformatics Institute, EMBL Cambridge Outstation, Cambridge CB10 1SD, UK. vkunin@lbl.gov
authors.forename: Victor
authors.initials: V
authors.lastname: Kunin
authors.forename: Sarah A
authors.initials: SA
authors.lastname: Teichmann
authors.forename: Martijn A
authors.initials: MA
authors.lastname: Huynen
authors.forename: Christos A
authors.initials: CA
authors.lastname: Ouzounis
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D030562:Databases, Protein; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D011506:Proteins / Q000032:analysis / Q000737:chemistry* / Q000145:classification*; D012107:Research Design; D016415:Sequence Alignment / Q000379:methods*; D017421:Sequence Analysis / Q000379:methods*
chemical_list: D011506:Proteins