title: Identification of proteases and their types.
abstract: Called by many as biology's version of Swiss army knives, proteases cut long sequences of amino acids into fragments and regulate most physiological processes. They are vitally important in the life cycle. Different types of proteases have different action mechanisms and biological processes. With the avalanche of protein sequences generated during the postgenomic age, it is highly desirable for both basic research and drug design to develop a fast and reliable method for identifying the types of proteases according to their sequences or even just for whether they are proteases or not. In this article, three recently developed identification methods in this regard are discussed: (i) FunD-PseAAC, (ii) GO-PseAAC, and (iii) FunD-PsePSSM. The first two were established by hybridizing the FunD (functional domain) approach and the GO (gene ontology) approach, respectively, with the PseAAC (pseudo amino acid composition) approach. The third method was established by fusing the FunD approach with the PsePSSM (pseudo position-specific scoring matrix) approach. Of these three methods, only FunD-PsePSSM has provided a server called ProtIdent (protease identifier), which is freely accessible to the public via the website at http://www.csbio.sjtu.edu.cn/bioinf/Protease. For the convenience of users, a step-by-step guide on how to use ProtIdent is illustrated. Meanwhile, the caveat in using ProtIdent and how to understand the success expectancy rate of a statistical predictor are discussed. Finally, the essence of why ProtIdent can yield a high success rate in identifying proteases and their types is elucidated.
authors.affiliation: Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240, China. hbshen@sjtu.edu.cn
authors.forename: Hong-Bin
authors.initials: HB
authors.lastname: Shen
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D010447:Peptide Hydrolases / Q000737:chemistry / Q000145:classification* / Q000378:metabolism*; D011237:Predictive Value of Tests; D020539:Sequence Analysis, Protein
chemical_list: D010447:Peptide Hydrolases