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Protein classification using sequential pattern mining.

Themis P Exarchos1, Costas Papaloukas, Christos Lampros

  • 1Dept. of Comput. Sci., Ioannina Univ., Greece. me01238@cc.uio.gr

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|October 20, 2007
PubMed
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This study uses sequential pattern mining (SPM) to classify protein structures based on their amino acid sequences. The method accurately categorizes proteins into structural folds, aiding in understanding newly discovered proteins.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Protein classification is crucial for understanding protein function and structure.
  • Sequence-based methods are valuable for predicting protein folds, especially for newly discovered proteins.
  • Sequential Pattern Mining (SPM) offers a novel approach to analyze protein primary structures.

Purpose of the Study:

  • To apply sequential pattern mining (SPM) for protein fold recognition using primary structure analysis.
  • To develop and evaluate a classifier that utilizes extracted sequential patterns for protein fold categorization.
  • To assess the accuracy of the proposed SPM-based methodology in classifying proteins into structural folds.

Main Methods:

  • Utilized the cSPADE algorithm, an efficient SPM technique, for analyzing protein primary sequences.

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  • Extracted sequential patterns from protein sequences to represent structural features.
  • Developed a classifier to categorize proteins into appropriate fold classes based on the identified sequential patterns.

    • Main Results:

    • The proposed methodology achieved an overall accuracy of 36% across 17 distinct protein fold categories.
    • Classification performance improved significantly when focusing on the top three most probable protein folds, reaching up to 65% accuracy.
    • Sequential pattern mining proved effective in identifying patterns relevant to protein fold classification.

    Conclusions:

    • Sequential pattern mining is a viable method for sequence-based protein fold recognition.
    • The developed classifier demonstrates potential for aiding in the structural and functional annotation of unknown proteins.
    • Further refinement of the methodology could enhance classification accuracy for a broader range of protein folds.