title: Exploiting structural classifications for function prediction: towards a domain grammar for protein function.
abstract: The ability to assign function to proteins has become a major bottleneck for comprehensively understanding cellular mechanisms at the molecular level. Here we discuss the extent to which structural domain classifications can help in deciphering the complex relationship between the functions of proteins and their sequences and structures. Structural classifications are particularly helpful in understanding the mosaic manner in which new proteins and functions emerge through evolution. This is partly because they provide reliable and concrete domain definitions and enable the detection of very remote structural similarities and homologies. It is also because structural data can illuminate more clearly the mechanisms by which a broader functional repertoire can emerge during evolution.
authors.affiliation: Department of Structural and Molecular Biology, University College London, London WC1E 6BT, United Kingdom.
authors.forename: Benoît H
authors.initials: BH
authors.lastname: Dessailly
authors.forename: Oliver C
authors.initials: OC
authors.lastname: Redfern
authors.forename: Alison
authors.initials: A
authors.lastname: Cuff
authors.forename: Christine A
authors.initials: CA
authors.lastname: Orengo
mesh_terms: D000818:Animals; D006801:Humans; D008958:Models, Molecular; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*
chemical_list: D011506:Proteins