Using a New Alignment Kernel Function to Identify Secretory Proteins
Authors: Liu, Hui; Yang, Jie; Liu, Dan-Qing; Shen, Hong-Bin; Chou, Kuo-Chen
Source: Protein and Peptide Letters, Volume 14, Number 2, February 2007 , pp. 203-208(6)
Publisher: Bentham Science Publishers
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.
Document Type: Research Article
Affiliations: Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, 200030, China.
Publication date: February 1, 2007
- Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.