ArticlePloS one2012
Prediction of protein phosphorylation sites by using the composition of k-spaced amino acid pairs.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information.PeerJ · 2022Article
- pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework.Frontiers in cell and developmental biology · 2021Article
- Article
- Protein kinases phosphorylate long disordered regions in intrinsically disordered proteins.Protein science : a publication of the Protein Society · 2020Article
- Article
- Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites.BMC bioinformatics · 2019Article
- An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation.Frontiers in genetics · 2019Article
- Precise Prediction of Calpain Cleavage Sites and Their Aberrance Caused by Mutations in Cancer.Frontiers in genetics · 2019Article
- PTM-ssMP: A Web Server for Predicting Different Types of Post-translational Modification Sites Using Novel Site-specific Modification Profile.International journal of biological sciences · 2018Article
- Crysalis: an integrated server for computational analysis and design of protein crystallization.Scientific reports · 2016Article
- Article
- Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites.Scientific reports · 2015Article
- Computational Identification of Protein Pupylation Sites by Using Profile-Based Composition of k-Spaced Amino Acid Pairs.PloS one · 2015Article
- Predict and Analyze Protein Glycation Sites with the mRMR and IFS Methods.BioMed research international · 2015Article
- A reliability-based track fusion algorithm.PloS one · 2015Article
- An ensemble method approach to investigate kinase-specific phosphorylation sites.International journal of nanomedicine · 2014Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As one of the most widespread protein post-translational modifications, phosphorylation is involved in many biological processes such as cell cycle, apoptosis. Identification of phosphorylated substrates and their corresponding sites will facilitate the understanding of the molecular mechanism of phosphorylation. Comparing with the labor-intensive and time-consuming experiment approaches, computational prediction of phosphorylation sites is much desirable due to their convenience and fast speed. In this paper, a new bioinformatics tool named CKSAAP_PhSite was developed that ignored the kinase information and only used the primary sequence information to predict protein phosphorylation sites. The highlight of CKSAAP_PhSite was to utilize the composition of k-spaced amino acid pairs as the encoding scheme, and then the support vector machine was used as the predictor. The performance of CKSAAP_PhSite was measured with a sensitivity of 84.81%, a specificity of 86.07% and an accuracy of 85.43% for serine, a sensitivity of 78.59%, a specificity of 82.26% and an accuracy of 80.31% for threonine as well as a sensitivity of 74.44%, a specificity of 78.03% and an accuracy of 76.21% for tyrosine. Experimental results obtained from cross validation and independent benchmark suggested that our method was very promising to predict phosphorylation sites and can be served as a useful supplement tool to the community. For public access, CKSAAP_PhSite is available at http://59.73.198.144/cksaap_phsite/.
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