Protein & Peptide Letters

Author(s): Hui Liu, Jie Yang, Dan-Qing Liu, Hong-Bin Shen and Kuo-Chen Chou

DOI: 10.2174/092986607779816087

Using a New Alignment Kernel Function to Identify Secretory Proteins

Page: [203 - 208] Pages: 6

  • * (Excluding Mailing and Handling)

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.

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