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Amino Acids

, Volume 35, Issue 3, pp 591–598 | Cite as

Using Chou’s pseudo amino acid composition to predict protein quaternary structure: a sequence-segmented PseAAC approach

  • Shao-Wu Zhang
  • Wei Chen
  • Feng Yang
  • Quan Pan
Original Article

Abstract

In the protein universe, many proteins are composed of two or more polypeptide chains, generally referred to as subunits, which associate through noncovalent interactions and, occasionally, disulfide bonds to form protein quaternary structures. It has long been known that the functions of proteins are closely related to their quaternary structures; some examples include enzymes, hemoglobin, DNA polymerase, and ion channels. However, it is extremely labor-expensive and even impossible to quickly determine the structures of hundreds of thousands of protein sequences solely from experiments. Since the number of protein sequences entering databanks is increasing rapidly, it is highly desirable to develop computational methods for classifying the quaternary structures of proteins from their primary sequences. Since the concept of Chou’s pseudo amino acid composition (PseAAC) was introduced, a variety of approaches, such as residue conservation scores, von Neumann entropy, multiscale energy, autocorrelation function, moment descriptors, and cellular automata, have been utilized to formulate the PseAAC for predicting different attributes of proteins. Here, in a different approach, a sequence-segmented PseAAC is introduced to represent protein samples. Meanwhile, multiclass SVM classifier modules were adopted to classify protein quaternary structures. As a demonstration, the dataset constructed by Chou and Cai [(2003) Proteins 53:282–289] was adopted as a benchmark dataset. The overall jackknife success rates thus obtained were 88.2–89.1%, indicating that the new approach is quite promising for predicting protein quaternary structure.

Keywords

Sequence-segmented PseAAC Residue conservation Von Neumann entropy Multiscale energy Moment descriptor Support vector machine 

Notes

Acknowledgments

The authors would like to thank Prof. Kuo-Chen Chou (Gordon Life Science Institute, San Diego, California, USA) for providing the datasets. 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).

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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.College of AutomationNorthwestern Polytechnical UniversityXi’anChina

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