Journal of Protein Chemistry

, Volume 22, Issue 4, pp 395–402

Application of Pseudo Amino Acid Composition for Predicting Protein Subcellular Location: Stochastic Signal Processing Approach

  • Yu-Xi Pan
  • Zhi-Zhou Zhang
  • Zong-Ming Guo
  • Guo-Yin Feng
  • Zhen-De Huang
  • Lin He
Article

Abstract

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.

Quasi-sequence order effect covariant-discriminant algorithm Mahalanobis distance Chou's invariance theorem low/high-pass Butterworth filter bioinformatics proteomics 

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Copyright information

© Plenum Publishing Corporation 2003

Authors and Affiliations

  • Yu-Xi Pan
    • 1
  • Zhi-Zhou Zhang
    • 1
  • Zong-Ming Guo
    • 1
  • Guo-Yin Feng
    • 1
  • Zhen-De Huang
    • 1
  • Lin He
    • 1
    • 2
  1. 1.Bio-X Life Science Research CenterShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Chinese Academy of ScienceShanghai Institutes for Biological SciencesShanghaiChina

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