Amino Acids

, Volume 34, Issue 4, pp 565–572 | Cite as

Using the concept of Chou’s pseudo amino acid composition to predict protein subcellular localization: an approach by incorporating evolutionary information and von Neumann entropies

  • Shao-Wu Zhang
  • Yun-Long Zhang
  • Hui-Fang Yang
  • Chun-Hui Zhao
  • Quan Pan
Original Article

Abstract

The rapidly increasing number of sequence entering into the genome databank has called for the need for developing automated methods to analyze them. Information on the subcellular localization of new found protein sequences is important for helping to reveal their functions in time and conducting the study of system biology at the cellular level. Based on the concept of Chou’s pseudo-amino acid composition, a series of useful information and techniques, such as residue conservation scores, von Neumann entropies, multi-scale energy, and weighted auto-correlation function were utilized to generate the pseudo-amino acid components for representing the protein samples. Based on such an infrastructure, a hybridization predictor was developed for identifying uncharacterized proteins among the following 12 subcellular localizations: chloroplast, cytoplasm, cytoskeleton, endoplasmic reticulum, extracell, Golgi apparatus, lysosome, mitochondria, nucleus, peroxisome, plasma membrane, and vacuole. Compared with the results reported by the previous investigators, higher success rates were obtained, suggesting that the current approach is quite promising, and may become a useful high-throughput tool in the relevant areas.

Keywords

Chou’s pseudo-amino acid composition Residue evolutionary conservation von Neumann entropies Multi-scale energy Weighted auto-correlation function 

Abbreviations

Chou’s PseAA composition

Chou’s pseudo-amino acid composition

MSA

Multiple sequence alignments

VNE

von Neumann entropy

IS

Information score

MSE

Multi-scale energy

AAC

Amino acid composition

JACK

Jackknife tests

INDE

Independent dataset tests

MD

Moment descriptors

SVM

Support vector machine

Notes

Acknowledgments

This paper was supported in part by the National Natural Science Foundation of China (No. 60775012 and 60634030) and the Technological Innovation Foundation of Northwestern Polytechnical University (No. KC02), and the Science Technology Research and Development Program of Shaanxi (No. 2006k04-G14).

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Shao-Wu Zhang
    • 1
  • Yun-Long Zhang
    • 2
  • Hui-Fang Yang
    • 1
  • Chun-Hui Zhao
    • 1
  • Quan Pan
    • 1
  1. 1.College of AutomationNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Department of ComputerFirst Aeronautical Institute of Air ForceXinyangChina

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