title: Prediction of peptidase category based on functional domain composition.
abstract: Peptidases play pivotal regulatory roles in conception, birth, digestion, growth, maturation, aging, and death of all organisms. These regulatory roles include activation, synthesis and turnover of proteins. In the proteomics era, computational methods to identify peptidases and catalog the peptidases into six different major classes-aspartic peptidases, cysteine peptidases, glutamic peptidases, metallo peptidases, serine peptidases and threonine peptidases can give an instant glance at the biological functions of a newly identified protein. In this contribution, by combining the nearest neighbor algorithm and the functional domain composition, we introduce both an automatic peptidase identifier and an automatic peptidase classier. The successful identification and classification rates are 93.7% and 96.5% for our peptidase identifier and peptidase classifier, respectively. Free online peptidase identifier and peptidase classifier are provided on our Web page http://pcal.biosino.org/protease_classification.html.
authors.affiliation: CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China
authors.forename: Xiaochun
authors.initials: X
authors.lastname: Xu
authors.forename: Dong
authors.initials: D
authors.lastname: Yu
authors.forename: Wei
authors.initials: W
authors.lastname: Fang
authors.forename: Yushao
authors.initials: Y
authors.lastname: Cheng
authors.forename: Ziliang
authors.initials: Z
authors.lastname: Qian
authors.forename: Wencong
authors.initials: W
authors.lastname: Lu
authors.forename: Yudong
authors.initials: Y
authors.lastname: Cai
authors.forename: Kaiyan
authors.initials: K
authors.lastname: Feng