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Using a New Alignment Kernel Function to Identify Secretory Proteins

[ Vol. 14 , Issue. 2 ]


Hui Liu, Jie Yang, Dan-Qing Liu, Hong-Bin Shen and Kuo-Chen Chou   Pages 203 - 208 ( 6 )


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.


Kernel function, global alignment, support vector machine, signal sequence, cleavage site, scaled window


Institute of Image Processing&Pattern Recognition, Shanghai Jiaotong University, 200030, China.

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