Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach

The function of a protein is closely correlated with its subcellular location. With the success of human genome project and the rapid increase in the number of newly found protein sequences entering into data banks, it is highly desirable to develop an automated method for predicting the subcellular...

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Bibliographic Details
Published in:Journal of Protein Chemistry 2003-05, Vol.22 (4), p.395-402
Main Authors: Pan, Yu-Xi, Zhang, Zhi-Zhou, Guo, Zong-Ming, Feng, Guo-Yin, Huang, Zhen-De, He, Lin
Format: Article
Language:English
Subjects:
Algorithms
Amino acid composition
Amino acids
Amino Acids - analysis
Biotechnology
Cells - cytology
Cells - metabolism
Composition
Computational Biology - methods
Digital signal processing
Digital signal processors
Drug development
Genomics
Human Genome Project
Humans
Mathematical analysis
Protein composition
Protein Transport
Proteins
Proteins - chemistry
Proteins - metabolism
Sequences
Signal processing
Stochastic Processes
Stochasticity
Subcellular Fractions - chemistry
Subcellular Fractions - metabolism
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Summary:The function of a protein is closely correlated with its subcellular location. With the success of human genome project and the rapid increase in the number of newly found protein sequences entering into data banks, it is highly desirable to develop an automated method for predicting the subcellular location of proteins. The establishment of such a predictor will no doubt expedite the functionality determination of newly found proteins and the process of prioritizing genes and proteins identified by genomics efforts as potential molecular targets for drug design. Based on the concept of pseudo amino acid composition originally proposed by K. C. Chou (Proteins: Struct. Funct. Genet. 43: 246-255, 2001), the digital signal processing approach has been introduced to partially incorporate the sequence order effect. One of the remarkable merits by doing so is that many existing tools in mathematics and engineering can be straightforwardly used in predicting protein subcellular location. The results thus obtained are quite encouraging. It is anticipated that the digital signal processing may serve as a useful vehicle for many other protein science areas as well.
ISSN:0277-8033
1572-3887
1573-4943
DOI:10.1023/A:1025350409648