Using a new alignment kernel function to identify secretory proteins.
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.
Liu
Hui
H
Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, 200030, China. sh_liuhui@sjtu.edu.cn
Yang
Jie
J
Liu
Dan-Qing
DQ
Shen
Hong-Bin
HB
Chou
Kuo-Chen
KC
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*
D021382:Protein Sorting Signals; D011506:Proteins