title: A new similarity measure among protein sequences.
abstract: Protein sequence analysis is an important tool to decode the logic of life. One of the most important similarity measures in this area is the edit distance between amino acids of two sequences. We believe this criterion should be reconsidered because protein features are probably associated more with small peptide fragments than with individual amino acids. In this paper, we design small patterns that are associated with highly conversed regions among a set of protein sequences. These patterns are used analogous to the index terms in information retrieval. Therefore, we do not consider gaps within patterns. This new similarity measure has been applied to phylogenetic tree construction, protein clustering and protein secondary structure prediction and has produced promising results.
authors.affiliation: Institute of Information Sciences, Academia Sinica, Taipei, 115, Taiwan. kpw@iis.sinica.edu.tw
authors.forename: Kuen-Pin
authors.initials: KP
authors.lastname: Wu
authors.forename: Hsin-Nan
authors.initials: HN
authors.lastname: Lin
authors.forename: Ting-Yi
authors.initials: TY
authors.lastname: Sung
authors.forename: Wen-Lian
authors.initials: WL
authors.lastname: Hsu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D017124:Conserved Sequence; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012984:Software
chemical_list: D011506:Proteins