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

Authors

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

DOI: 10.1023/A:1025350409648

Cite this article as:
Pan, Y., Zhang, Z., Guo, Z. et al. J Protein Chem (2003) 22: 395. doi:10.1023/A:1025350409648

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 effectcovariant-discriminant algorithmMahalanobis distanceChou's invariance theoremlow/high-pass Butterworth filterbioinformaticsproteomics

Copyright information

© Plenum Publishing Corporation 2003