Condensing position-specific scoring matrixs by the Kidera factors for ligand-binding site prediction

Chun Fang, Tamotsu Noguchi, Hayato Yamana

    Research output: Contribution to journalArticle

    3 Citations (Scopus)

    Abstract

    Position-specific scoring matrix (PSSM) has been widely used for identifying protein functional sites. However, it is 20-dimentional and contains many redundant features. The Kidera factors were reported to contain information relating almost all physical properties of amino acids, but it requires appropriate weighting coefficients to express their properties. We developed a novel method, named as KSPSSMpred, which integrated PSSM and the Kidera Factors into a 10-dimensional matrix (KSPSSM) for ligandbinding site prediction. Flavin adenine dinucleotide (FAD) was chosen as a representative ligand for this study. When compared with five other featurebased methods on a benchmark dataset, KSPSSMpred performed the best. This study demonstrates that, KSPSSM is an effective feature extraction method which can enrich PSSM with information relating 188 physical properties of residues, and reduce 50% feature dimensions without losing information included in the PSSM.

    Original languageEnglish
    Pages (from-to)70-84
    Number of pages15
    JournalInternational Journal of Data Mining and Bioinformatics
    Volume12
    Issue number1
    DOIs
    Publication statusPublished - 2015

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    Keywords

    • Kidera factors
    • Ligand-binding site
    • Position specific scoring matrix

    ASJC Scopus subject areas

    • Library and Information Sciences
    • Information Systems
    • Biochemistry, Genetics and Molecular Biology(all)

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