Exploring Potential Discriminatory Information Embedded in PSSM to Enhance Protein Structural Class Prediction Accuracy

  • Abdollah Dehzangi
  • Kuldip Paliwal
  • James Lyons
  • Alok Sharma
  • Abdul Sattar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7986)


Determining the structural class of a given protein can provide important information about its functionality and its general tertiary structure. In the last two decades, the protein structural class prediction problem has attracted tremendous attention and its prediction accuracy has been significantly improved. Features extracted from the Position Specific Scoring Matrix (PSSM) have played an important role to achieve this enhancement. However, this information has not been adequately explored since the protein structural class prediction accuracy relying on PSSM for feature extraction still remains limited. In this study, to explore this potential, we propose segmentation-based feature extraction technique based on the concepts of amino acids’ distribution and auto covariance. By applying a Support Vector Machine (SVM) to our extracted features, we enhance protein structural class prediction accuracy up to 16% over similar studies found in the literature. We achieve over 90% and 80% prediction accuracies for 25PDB and 1189 benchmarks respectively by solely relying on the PSSM for feature extraction.


Protein Structural Class Prediction Problem Feature Extraction Segmented distribution Segmented Auto Covariance Support Vector Machine (SVM) 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Abdollah Dehzangi
    • 1
    • 2
  • Kuldip Paliwal
    • 1
  • James Lyons
    • 1
  • Alok Sharma
    • 3
  • Abdul Sattar
    • 1
    • 2
  1. 1.Institute for Integrated and Intelligent Systems (IIIS)Griffith UniversityBrisbaneAustralia
  2. 2.National ICT Australia (NICTA)BrisbaneAustralia
  3. 3.University of the South PacificFiji

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