KSCX2-EW-G-8), and the Tianjin Municipal Science & Technology Commission (No. S11156), the Knowledge Innovative Program of CAS (No. LP110200333), the Hundred Talents Program of the Chinese Academy of Sciences (CAS), the Japan Society for the Promotion of Science (JSPS) (No. 490989), the Australian Research Council (ARC) (No. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.įunding: This work was supported by grants from the National Health and Medical Research Council of Australia (NHMRC) (No. Received: OctoAccepted: DecemPublished: February 2, 2012Ĭopyright: © 2012 Song et al. PLoS ONE 7(2):Įditor: Christian Schönbach, Kyushu Institute of Technology, Japan Ĭitation: Song J, Tan H, Wang M, Webb GI, Akutsu T (2012) TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences. 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. 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. 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° and 44.6°, respectively, which are 1% and 3% respectively lower than that using one of the state-of-the-art prediction tools ANGLOR. ![]() 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. In this study, we develop a new approach called TANGLE (Torsion ANGLE predictor) to predict the protein backbone torsion angles from amino acid sequences. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures. Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the C α-N bond (Phi) and the C α-C bond (Psi).
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