TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences
文献类型：期刊论文
| 作者 | Song, Jiangning1,2,3,4; Tan, Hao1; Wang, Mingjun2,3; Webb, Geoffrey I.5; Akutsu, Tatsuya4 |
| 刊名 | PLOS ONE
 |
| 出版日期 | 2012-02-02 |
| 卷号 | 7期号:2页码:e30361 |
| 英文摘要 | Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the C-alpha-N bond (Phi) and the C-alpha-C bond (Psi). Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures. In this study, we develop a new approach called TANGLE (Torsion ANGLE predictor) to predict the protein backbone torsion angles from amino acid sequences. TANGLE uses a two-level support vector regression approach to perform real-value torsion angle prediction using a variety of features derived from amino acid sequences, including the evolutionary profiles in the form of position-specific scoring matrices, predicted secondary structure, solvent accessibility and natively disordered region as well as other global sequence features. When evaluated based on a large benchmark dataset of 1,526 non-homologous proteins, the mean absolute errors (MAEs) of the Phi and Psi angle prediction are 27.8 degrees and 44.6 degrees, respectively, which are 1% and 3% respectively lower than that using one of the state-of-the-art prediction tools ANGLOR. Moreover, the prediction of TANGLE is significantly better than a random predictor that was built on the amino acid-specific basis, with the p-value, 1.46e-147 and 7.97e-150, respectively by the Wilcoxon signed rank test. As a complementary approach to the current torsion angle prediction algorithms, TANGLE should prove useful in predicting protein structural properties and assisting protein fold recognition by applying the predicted torsion angles as useful restraints. TANGLE is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/similar to sjn/TANGLE/. |
| WOS标题词 | Science & Technology |
| 类目[WOS] | Multidisciplinary Sciences |
| 研究领域[WOS] | Science & Technology - Other Topics |
| 关键词[WOS] | SECONDARY STRUCTURE PREDICTION ; ACCESSIBLE SURFACE-AREAS ; REAL-VALUE PREDICTION ; EVOLUTIONARY INFORMATION ; SOLVENT ACCESSIBILITY ; DIHEDRAL ANGLES ; NEURAL-NETWORKS ; LOCAL-STRUCTURE ; WEB-SERVER ; PSI-BLAST |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000301979000004 |
| 公开日期 | 2012-05-22 |
| 源URL | [http://localhost/handle/0/230]  |
| 专题 | 天津工业生物技术研究所_结构生物信息学和整合系统生物学实验室 宋江宁_期刊论文 |
| 作者单位 | 1.Monash Univ, Fac Med, Dept Biochem & Mol Biol, Melbourne, Vic 3004, Australia 2.Chinese Acad Sci, Tianjin Inst Ind Biotechnol, Key Lab Syst Microbial Biotechnol, Tianjin, Peoples R China 3.Chinese Acad Sci, Tianjin Inst Ind Biotechnol, Natl Engn Lab Ind Enzymes, Tianjin, Peoples R China 4.Kyoto Univ, Inst Chem Res, Bioinformat Ctr, Uji, Kyoto, Japan 5.Monash Univ, Fac Informat Technol, Melbourne, Vic 3004, Australia |
| 推荐引用方式 GB/T 7714 | Song, Jiangning,Tan, Hao,Wang, Mingjun,et al. TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences[J]. PLOS ONE,2012,7(2):e30361. |
| APA | Song, Jiangning,Tan, Hao,Wang, Mingjun,Webb, Geoffrey I.,&Akutsu, Tatsuya.(2012).TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences.PLOS ONE,7(2),e30361. |
| MLA | Song, Jiangning,et al."TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences".PLOS ONE 7.2(2012):e30361. |
入库方式： OAI收割
来源：天津工业生物技术研究所
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