TY - JOUR
T1 - Condensing position-specific scoring matrixs by the Kidera factors for ligand-binding site prediction
AU - Fang, Chun
AU - Noguchi, Tamotsu
AU - Yamana, Hayato
N1 - Publisher Copyright:
Copyright © 2015 Inderscience Enterprises Ltd.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Kidera factors
KW - Ligand-binding site
KW - Position specific scoring matrix
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U2 - 10.1504/IJDMB.2015.068954
DO - 10.1504/IJDMB.2015.068954
M3 - Article
C2 - 26489143
AN - SCOPUS:84928801306
VL - 12
SP - 70
EP - 84
JO - International Journal of Data Mining and Bioinformatics
JF - International Journal of Data Mining and Bioinformatics
SN - 1748-5673
IS - 1
ER -