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

Lin, Shoukai; Song, Qi; Huan, Tao; Wang, Wei; Wan, Weifeng; Huang, Jian; Xu, Chaoqun; Chebii, Vivien J; Kitony, Justine; Que, Shufu; Harrison, Andrew; He, Hongwen

doi:10.1038/srep11940

Cited by 19 publications

(12 citation statements)

References 38 publications

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“…More importantly, the revealed phosphosites of these transcription factors provided valuable information for studies to elucidate the mechanism of these proteins in ABA signaling. For example, bZIP72 harbors over 40 potential phosphosites as predicted by Rice Phospho 1.0 tool [ 58 ]. However, only the Ser71 and Ser116 of bZIP72 were detected and up-phosphrylated under ABA treatment in our phosphoproteomic data ( Table 1 ).…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Proteomic Survey of ABA-Induced Protein Phosphorylation in Rice (Oryza sativa L.)

Qiu

Hou

Wang

et al. 2017

IJMS

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abscisic acid (ABA) is a key phytohormone regulating plant development and stress response. The signal transduction of ABA largely relies on protein phosphorylation. However; little is known about the phosphorylation events occurring during ABA signaling in rice thus far. By employing a label-free; MS (Mass Spectrometry)-based phosphoproteomic approach; we identified 2271 phosphosites of young rice seedlings and their intensity dynamics in response to ABA; during which 1060 proteins were found to be differentially phosphorylated. Western-blot analysis verified the differential phosphorylation pattern of D1, SMG1 and SAPK9 as indicated by the MS result; suggesting the high reliability of our phosphoproteomic data. The DP (differentially phosphorylated) proteins are extensively involved in ABA as well as other hormone signaling pathways. It is suggested that ABA antagonistically regulates brassinosteroid (BR) signaling via inhibiting BR receptor activity. The result of this study not only expanded our knowledge of rice phosphoproteome, but also shed more light on the pattern of protein phosphorylation in ABA signaling.

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Section: Discussionmentioning

confidence: 99%

A Comprehensive Proteomic Survey of ABA-Induced Protein Phosphorylation in Rice (Oryza sativa L.)

Qiu

Hou

Wang

et al. 2017

IJMS

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“…Furthermore, the experimentally identified phosphorylation sites in rice, which were collected in our previous research work [15], were also used to detect the potential phosSNPs. Due to the limited information of PK specific for rice protein phosphorylation sites, we only predicted Type I and II phosSNPs in the identified rice phosphorylation sites.…”

Section: Resultsmentioning

confidence: 99%

“…However, to our best knowledge, the potential impact of SNPs on the protein phosphorylation status in rice is not clearly understood. In plants, phosphorylation is one of the most important post-translational modifications (PTMs) of proteins that have essential roles in the majority of biological pathways, regulating cellular processes like metabolism, proliferation, differentiation and apoptosis [15]. A large number of phosphorylation sites in rice had been identified by Nakagami et al [16].…”

Section: Introductionmentioning

confidence: 99%

“…A large number of phosphorylation sites in rice had been identified by Nakagami et al [16]. Moreover, a rice-specific phosphorylation site predictor, Rice_phospho 1.0, had been developed [15]. The resources of SNPs and phosphorylation sites in rice genome and proteome could contribute to the comprehensive studies of the impact of SNPs on protein phosphorylation status.…”

Section: Introductionmentioning

confidence: 99%

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Impact of SNPs on Protein Phosphorylation Status in Rice (Oryza sativa L.)

Lin

Chen

Huan

et al. 2016

IJMS

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Single nucleotide polymorphisms (SNPs) are widely used in functional genomics and genetics research work. The high-quality sequence of rice genome has provided a genome-wide SNP and proteome resource. However, the impact of SNPs on protein phosphorylation status in rice is not fully understood. In this paper, we firstly updated rice SNP resource based on the new rice genome Ver. 7.0, then systematically analyzed the potential impact of Non-synonymous SNPs (nsSNPs) on the protein phosphorylation status. There were 3,897,312 SNPs in Ver. 7.0 rice genome, among which 9.9% was nsSNPs. Whilst, a total 2,508,261 phosphorylated sites were predicted in rice proteome. Interestingly, we observed that 150,197 (39.1%) nsSNPs could influence protein phosphorylation status, among which 52.2% might induce changes of protein kinase (PK) types for adjacent phosphorylation sites. We constructed a database, SNP_rice, to deposit the updated rice SNP resource and phosSNPs information. It was freely available to academic researchers at . As a case study, we detected five nsSNPs that potentially influenced heterotrimeric G proteins phosphorylation status in rice, indicating that genetic polymorphisms showed impact on the signal transduction by influencing the phosphorylation status of heterotrimeric G proteins. The results in this work could be a useful resource for future experimental identification and provide interesting information for better rice breeding.

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“…Lin et al . 9 developed a rice-specific SVM predictor called Rice_Phospho by combining amino acid occurrence frequency with composition of k -spaced amino acid pairs. To identify kinase-specific phosphorylation site, Huang et al .…”

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confidence: 99%

Prediction of phosphothreonine sites in human proteins by fusing different features

Zhao

Lai

Tang

et al. 2016

Sci Rep

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Phosphorylation is one of the most important protein post-translation modifications. With the rapid development of high-throughput mass spectrometry, phosphorylation site data is rapidly accumulating, which provides us an opportunity to systematically investigate and predict phosphorylation in proteins. The phosphorylation of threonine is the addition of a phosphoryl group to its polar side chains group. In this work, we statistically analyzed the distribution of the different properties including position conservation, secondary structure, accessibility and some other physicochemical properties of the residues surrounding the phosphothreonine site and non-phosphothreonine site. We found that the distributions of those features are non-symmetrical. Based on the distribution of properties, we developed a new model by using optimal window size strategy and feature selection technique. The cross-validated results show that the area under receiver operating characteristic curve reaches to 0.847, suggesting that our model may play a complementary role to other existing methods for predicting phosphothreonine site in proteins.

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

Cited by 19 publications

References 38 publications

A Comprehensive Proteomic Survey of ABA-Induced Protein Phosphorylation in Rice (Oryza sativa L.)

A Comprehensive Proteomic Survey of ABA-Induced Protein Phosphorylation in Rice (Oryza sativa L.)

Impact of SNPs on Protein Phosphorylation Status in Rice (Oryza sativa L.)

Prediction of phosphothreonine sites in human proteins by fusing different features

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