KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns

By Y. Wong, Tzong-Yi Lee, Han-Kuen Liang, Chia-Mao Huang, Ting-Yuan Wang, Yi-Huan Yang, Chia-Huei Chu, Hsien-Da Huang, M. Ko, Jenn-Kang Hwang. Nucleic Acids Res., 2007

347 citations recorded by the source corpus.

Due to the importance of protein phosphorylation in cellular control, many researches are undertaken to predict the kinase-specific phosphorylation sites. Referred to our previous work, KinasePhos 1.0, incorporated profile hidden Markov model (HMM) with flanking residues of the kinase-specific phosphorylation sites. Herein, a new web server, KinasePhos 2.0, incorporates support vector machines (SVM) with the protein sequence profile and protein coupling pattern, which is a novel feature used for identifying phosphorylation sites. The coupling pattern [XdZ] denotes the amino acid coupling-pattern of amino acid types X and Z that are separated by d amino acids. The differences or quotients of coupling strength CXdZ between the positive set of phosphorylation sites and the background set of whole protein sequences from Swiss-Prot are computed to determine the number of coupling patterns for training SVM models. After the evaluation based on k-fold cross-validation and Jackknife cross-validation, the average predictive accuracy of phosphorylated serine, threonine, tyrosine and histidine are 90, 93, 88 and 93%, respectively. KinasePhos 2.0 performs better than other tools previously developed. The proposed web server is freely available at http://KinasePhos2.mbc.nctu.edu.tw/.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2f547096be82436370bd89676d4efe6e9f3794c1

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In Molecular Biology

1. Lars Juhl Jensen
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4. Ling-Yun Wu
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Citations in the corpus

• iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition (2013, methodology): “However, to reduce computational time, here let us adopt the 10-fold cross-validation to examine the prediction quality as done by many investigators for PTM sites prediction [63,64,65,66].”
• PhosContext2vec: a distributed representation of residue-level sequence contexts and its application to general and kinase-specific phosphorylation site prediction (2018, methodology): “While in the case of kinase-specific phosphorylation site prediction, the performance of PhosContext2vec was compared to that of GPS 3.0, MusiteDeep, Musite 1.0, NetPhos 3.1, KinasePhos 2.0 7,8 , PhosphoPredict 10 , and PhosphoPick 37 .”
• NetworKIN: a resource for exploring cellular phosphorylation networks (2007, methodology): “This has motivated the development of numerous computational methods for predicting kinase–substrate relations, for example, Scansite (4), NetphosK (5,6), PREDIKIN (7), PredPhospho (8) GPS (9), PPSP (10) and KinasePhos ( 11 ).”
• GPS 2.0, a Tool to Predict Kinase-specific Phosphorylation Sites in Hierarchy *S (2008, methodology): “And KinasePhos 1.0 uses a maximal dependence decomposition strategy and constructs a profile hidden Markov model for each kinase (15), whereas KinasePhos 2.0 retrieves the coupling patterns ( XdZ where amino acid types X and Z are separated by d amino acids) from the known phosphorylation sites and ”