title: Complementary classification approaches for protein sequences.
abstract: We have studied five methods of protein classification and have applied them to the 768 groups of related proteins in the PROSITE catalog. Four of these methods are based on searching a database of blocks, and the other uses the frequently occurring motifs found in the protein families combined with a fingerprint technique. Our experimental results show that the block-based methods perform well when taking into account the probability of amino acids occurring in a block. Furthermore, the five methods give information that is complementary to each other. Thus, using the five methods together, one can obtain high confidence classifications (if the results agree) or suggest alternative hypotheses (if the results disagree). We also list those proteins whose current families documented in the PROSITE catalog differ from those suggested by our results. There are remarkably few of them, which is a testimony to the quality of PROSITE.
authors.affiliation: Department of Computer and Information Science, New Jersey Institute of Technology, Newark 07102, USA.
authors.forename: J T
authors.initials: JT
authors.lastname: Wang
authors.forename: T G
authors.initials: TG
authors.lastname: Marr
authors.forename: D
authors.initials: D
authors.lastname: Shasha
authors.forename: B A
authors.initials: BA
authors.lastname: Shapiro
authors.forename: G W
authors.initials: GW
authors.lastname: Chirn
authors.forename: T Y
authors.initials: TY
authors.lastname: Lee
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D016208:Databases, Factual; D006801:Humans; D008969:Molecular Sequence Data; D011506:Proteins / Q000145:classification*; D012984:Software
chemical_list: D011506:Proteins