Amino Acids

, Volume 34, Issue 4, pp 565–572

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

DOI: 10.1007/s00726-007-0010-9

Cite this article as:
Zhang, SW., Zhang, YL., Yang, HF. et al. Amino Acids (2008) 34: 565. doi:10.1007/s00726-007-0010-9


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.


Chou’s pseudo-amino acid compositionResidue evolutionary conservationvon Neumann entropiesMulti-scale energyWeighted auto-correlation function


Chou’s PseAA composition

Chou’s pseudo-amino acid composition


Multiple sequence alignments


von Neumann entropy


Information score


Multi-scale energy


Amino acid composition


Jackknife tests


Independent dataset tests


Moment descriptors


Support vector machine

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