OH-PRED: prediction of protein hydroxylation sites by incorporating adapted normal distribution bi-profile Bayes feature extraction and physicochemical properties of amino acids
| Autor: | Cang-Zhi Jia, Yu-Hua Yao, Wen-Ying He |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Support Vector Machine Lysine Normal Distribution Web Browser Hydroxylation Cross-validation 03 medical and health sciences chemistry.chemical_compound Hydroxyproline Protein hydroxylation Structural Biology Humans Position-Specific Scoring Matrices Proline Amino Acids Molecular Biology chemistry.chemical_classification Chemistry Computational Biology Proteins Bayes Theorem General Medicine Amino acid Hydroxylysine 030104 developmental biology Biochemistry Algorithms Software |
| Zdroj: | Journal of Biomolecular Structure and Dynamics. 35:829-835 |
| ISSN: | 1538-0254 0739-1102 |
| Popis: | Hydroxylation of proline or lysine residues in proteins is a common post-translational modification event, and such modifications are found in many physiological and pathological processes. Nonetheless, the exact molecular mechanism of hydroxylation remains under investigation. Because experimental identification of hydroxylation is time-consuming and expensive, bioinformatics tools with high accuracy represent desirable alternatives for large-scale rapid identification of protein hydroxylation sites. In view of this, we developed a supporter vector machine-based tool, OH-PRED, for the prediction of protein hydroxylation sites using the adapted normal distribution bi-profile Bayes feature extraction in combination with the physicochemical property indexes of the amino acids. In a jackknife cross validation, OH-PRED yields an accuracy of 91.88% and a Matthew's correlation coefficient (MCC) of 0.838 for the prediction of hydroxyproline sites, and yields an accuracy of 97.42% and a MCC of 0.949 for the prediction of hydroxylysine sites. These results demonstrate that OH-PRED increased significantly the prediction accuracy of hydroxyproline and hydroxylysine sites by 7.37 and 14.09%, respectively, when compared with the latest predictor PredHydroxy. In independent tests, OH-PRED also outperforms previously published methods. |
| Databáze: | OpenAIRE |
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