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Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites.

Lin S, Song Q, Tao H, Wang W, Wan W, Huang J, Xu C, Chebii V, Kitony J, Que S, Harrison A, He H - Sci Rep (2015)

Bottom Line: Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site.A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms.Rice_Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC_Os03g51600.1, a protein sequence which did not appear in the training dataset.

View Article: PubMed Central - PubMed

Affiliation: College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.

ABSTRACT
Experimentally-determined or computationally-predicted protein phosphorylation sites for distinctive species are becoming increasingly common. In this paper, we compare the predictive performance of a novel classification algorithm with different encoding schemes to develop a rice-specific protein phosphorylation site predictor. Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site. A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms. We have used SVM with AF-CKSAAP to construct a rice-specific protein phosphorylation sites predictor, Rice_Phospho 1.0 (http://bioinformatics.fafu.edu.cn/rice_phospho1.0). We measure the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) of Rice_Phospho 1.0 to be 82.0% and 0.64, significantly higher than those measures for other predictors such as Scansite, Musite, PlantPhos and PhosphoRice. Rice_Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC_Os03g51600.1, a protein sequence which did not appear in the training dataset. In summary, Rice_phospho 1.0 outputs reliable predictions of protein phosphorylation sites in rice, and will serve as a useful tool to the community.

No MeSH data available.


ROC curves of predicting performance of Rice_Phospho 1.0 and PlantPhos.
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f4: ROC curves of predicting performance of Rice_Phospho 1.0 and PlantPhos.

Mentions: We used SVM with AF-CKSAAP to develop a new rice-specific predictor, Rice_Phospho 10. We applied the independent test dataset to compare the predicting performance of Rice_phospho 1.0 with the newly existing predictors, including Scansite, Musite, PlantPhos and PhosphoRice. The MCC of the prediction performance of Rice_phosphos 1.0 in comparison to Scansite, Musite and PhosphoRice were shown in Table 4. Rice_Phospho 1.0 had higher MCC value than the existing predictors, indicating that the performance of Rice_Phospho 1.0 was significantly better than that of Scansite, Musite and PhosphoRice. The Area Under ROC Curve (AUC) of Rice_Phospho1.0 was higher than that of PlantPhos (Fig. 4), implying that Rice_Phospho 1.0 also outperformed PlantPhos.


Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites.

Lin S, Song Q, Tao H, Wang W, Wan W, Huang J, Xu C, Chebii V, Kitony J, Que S, Harrison A, He H - Sci Rep (2015)

ROC curves of predicting performance of Rice_Phospho 1.0 and PlantPhos.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493637&req=5

f4: ROC curves of predicting performance of Rice_Phospho 1.0 and PlantPhos.
Mentions: We used SVM with AF-CKSAAP to develop a new rice-specific predictor, Rice_Phospho 10. We applied the independent test dataset to compare the predicting performance of Rice_phospho 1.0 with the newly existing predictors, including Scansite, Musite, PlantPhos and PhosphoRice. The MCC of the prediction performance of Rice_phosphos 1.0 in comparison to Scansite, Musite and PhosphoRice were shown in Table 4. Rice_Phospho 1.0 had higher MCC value than the existing predictors, indicating that the performance of Rice_Phospho 1.0 was significantly better than that of Scansite, Musite and PhosphoRice. The Area Under ROC Curve (AUC) of Rice_Phospho1.0 was higher than that of PlantPhos (Fig. 4), implying that Rice_Phospho 1.0 also outperformed PlantPhos.

Bottom Line: Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site.A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms.Rice_Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC_Os03g51600.1, a protein sequence which did not appear in the training dataset.

View Article: PubMed Central - PubMed

Affiliation: College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.

ABSTRACT
Experimentally-determined or computationally-predicted protein phosphorylation sites for distinctive species are becoming increasingly common. In this paper, we compare the predictive performance of a novel classification algorithm with different encoding schemes to develop a rice-specific protein phosphorylation site predictor. Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site. A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms. We have used SVM with AF-CKSAAP to construct a rice-specific protein phosphorylation sites predictor, Rice_Phospho 1.0 (http://bioinformatics.fafu.edu.cn/rice_phospho1.0). We measure the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) of Rice_Phospho 1.0 to be 82.0% and 0.64, significantly higher than those measures for other predictors such as Scansite, Musite, PlantPhos and PhosphoRice. Rice_Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC_Os03g51600.1, a protein sequence which did not appear in the training dataset. In summary, Rice_phospho 1.0 outputs reliable predictions of protein phosphorylation sites in rice, and will serve as a useful tool to the community.

No MeSH data available.