Chapter title |
Prediction of Protein Phosphorylation Sites by Integrating Secondary Structure Information and Other One-Dimensional Structural Properties
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Chapter number | 18 |
Book title |
Prediction of Protein Secondary Structure
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Published in |
Methods in molecular biology, January 2017
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DOI | 10.1007/978-1-4939-6406-2_18 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6404-8, 978-1-4939-6406-2
|
Authors |
Yongchao Dou, Bo Yao, Chi Zhang |
Abstract |
Studies on phosphorylation are important but challenging for both wet-bench experiments and computational studies, and accurate non-kinase-specific prediction tools are highly desirable for whole-genome annotation in a wide variety of species. Here, we describe a phosphorylation site prediction webserver, PhosphoSVM, that employs Support Vector Machine to combine protein secondary structure information and seven other one-dimensional structural properties, including Shannon entropy, relative entropy, predicted protein disorder information, predicted solvent accessible area, amino acid overlapping properties, averaged cumulative hydrophobicity, and subsequence k-nearest neighbor profiles. This method achieved AUC values of 0.8405/0.8183/0.7383 for serine (S), threonine (T), and tyrosine (Y) phosphorylation sites, respectively, in animals with a tenfold cross-validation. The model trained by the animal phosphorylation sites was also applied to a plant phosphorylation site dataset as an independent test. The AUC values for the independent test data set were 0.7761/0.6652/0.5958 for S/T/Y phosphorylation sites, respectively. This algorithm with the optimally trained model was implemented as a webserver. The webserver, trained model, and all datasets used in the current study are available at http://sysbio.unl.edu/PhosphoSVM . |
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Unknown | 2 | 100% |
Demographic breakdown
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Student > Bachelor | 1 | 50% |
Researcher | 1 | 50% |
Readers by discipline | Count | As % |
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Unknown | 1 | 50% |