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Rice-Phospho 1.0: A new rice-specific SVM predictor for protein phosphorylation sites

Lin, S and Song, Q and Tao, H and Wang, W and Wan, W and Huang, J and Xu, C and Chebii, V and Kitony, J and Que, S and Harrison, A and He, H (2015) 'Rice-Phospho 1.0: A new rice-specific SVM predictor for protein phosphorylation sites.' Scientific Reports, 5. ISSN 2045-2322

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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 ( 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.

Item Type: Article
Subjects: Q Science > QH Natural history > QH301 Biology
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Jim Jamieson
Date Deposited: 23 Jul 2015 23:48
Last Modified: 23 Jan 2019 00:18

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