Shew, Christopher Evans, Samari Tao, Xiuping Using Excel To Study The Relation Between Protein Dihedral Angle Omega And Backbone Length Copyright Â© 2017 European Foot and Ankle Society. Measurement of Phi angle on weight-bearing dorsoplantar radiograph showed an excellent reliability among orthopaedic surgeons in determining the position of the talar component in the axial plane. Moreover, the prediction of TANGLE is significantly better than a random predictor that was built on the amino acid-specific basis, with the p-value0.05). 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 27.8Â° and 44.6Â°, 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 CÎ±-N bond ( Phi) and the CÎ±-C bond (Psi).

Song, Jiangning Tan, Hao Wang, Mingjun Webb, Geoffrey I. TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences

Peptide backbone torsion angles