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 language | English |
|---|---|
| Pages (from-to) | 70-84 |
| Number of pages | 15 |
| Journal | International Journal of Data Mining and Bioinformatics |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 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)
Cite this
Condensing position-specific scoring matrixs by the Kidera factors for ligand-binding site prediction. / Fang, Chun; Noguchi, Tamotsu; Yamana, Hayato.
In: International Journal of Data Mining and Bioinformatics, Vol. 12, No. 1, 2015, p. 70-84.Research output: Contribution to journal › Article
}
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
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
UR - http://www.scopus.com/inward/record.url?scp=84928801306&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84928801306&partnerID=8YFLogxK
U2 - 10.1504/IJDMB.2015.068954
DO - 10.1504/IJDMB.2015.068954
M3 - Article
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 -