title: Using a new alignment kernel function to identify secretory proteins.
abstract: As the knowledge of protein signal peptides can be used to reprogram cells in a desired way for gene therapy, signal peptides have become a crucial tool for researchers to design new drugs for targeting a particular organelle to correct a specific defect. To effectively use such a technique, however, we have to develop an automated method for fast and accurately predicting signal peptides and their cleavage sites, particularly in the post-genomic era when the number of protein sequences is being explosively increased. To realize this, the first important thing is to discriminate secretory proteins from non-secretory proteins. On the basis of the Needleman-Wunsch algorithm, we proposed a new alignment kernel function. The novel approach can be effectively used to extract the statistical properties of protein sequences for machine learning, leading to a higher prediction success rate.
authors.affiliation: Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, 200030, China. sh_liuhui@sjtu.edu.cn
authors.forename: Hui
authors.initials: H
authors.lastname: Liu
authors.forename: Jie
authors.initials: J
authors.lastname: Yang
authors.forename: Dan-Qing
authors.initials: DQ
authors.lastname: Liu
authors.forename: Hong-Bin
authors.initials: HB
authors.lastname: Shen
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D008954:Models, Biological; D008969:Molecular Sequence Data; D021382:Protein Sorting Signals; D011506:Proteins / Q000737:chemistry; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D021382:Protein Sorting Signals; D011506:Proteins