Using a New Alignment Kernel Function to Identify Secretory Proteins

Title: Using a New Alignment Kernel Function to Identify Secretory Proteins

Volume: 14 Issue: 2

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

Affiliation:

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

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.

Cite this article as:

Liu Hui, Yang Jie, Liu Dan-Qing, Shen Hong-Bin and Chou Kuo-Chen, Using a New Alignment Kernel Function to Identify Secretory Proteins, Protein & Peptide Letters 2007; 14 (2) . https://dx.doi.org/10.2174/092986607779816087

DOI https://dx.doi.org/10.2174/092986607779816087	Print ISSN 0929-8665
Publisher Name Bentham Science Publisher	Online ISSN 1875-5305