TANGLE: Two-level support vector regression approach for protein backbone torsion angle prediction from primary sequences

Jiangning Song, Hao Tan, Mingjun Wang, Geoffrey I Webb, Tatsuya Akutsu

Research output: Contribution to journalArticleResearchpeer-review

25 Citations (Scopus)

Abstract

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
Original languageEnglish
Article numbere30361
Number of pages16
JournalPLoS ONE
Volume7
Issue number2
DOIs
Publication statusPublished - 2012

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