title: Is protein classification necessary? Toward alternative approaches to function annotation.
abstract: The current nonredundant protein sequence database contains over seven million entries and the number of individual functional domains is significantly larger than this value. The vast quantity of data associated with these proteins poses enormous challenges to any attempt at function annotation. Classification of proteins into sequence and structural groups has been widely used as an approach to simplifying the problem. In this article we question such strategies. We describe how the multifunctionality and structural diversity of even closely related proteins confounds efforts to assign function on the basis of overall sequence or structural similarity. Rather, we suggest that strategies that avoid classification may offer a more robust approach to protein function annotation.
authors.affiliation: Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics, Columbia University, New York, NY 10032, USA.
authors.forename: Donald
authors.initials: D
authors.lastname: Petrey
authors.forename: Barry
authors.initials: B
authors.lastname: Honig
mesh_terms: D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry / Q000145:classification* / Q000378:metabolism*
chemical_list: D011506:Proteins