![]() 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 Cα-N bond ( Phi) and the Cα-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
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June 2023
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