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TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences

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

Abstract

Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the Cα-N bond (Phi) and the Cα-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° and 44.6°, 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

Suggested Citation

  • Jiangning Song & Hao Tan & Mingjun Wang & Geoffrey I Webb & Tatsuya Akutsu, 2012. "TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-16, February.
    • Handle: RePEc:plo:pone00:0030361
    DOI: 10.1371/journal.pone.0030361
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    References listed on IDEAS

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    1. Mridul K Kalita & Umesh K Nandal & Ansuman Pattnaik & Anandhan Sivalingam & Gowthaman Ramasamy & Manish Kumar & Gajendra P S Raghava & Dinesh Gupta, 2008. "CyclinPred: A SVM-Based Method for Predicting Cyclin Protein Sequences," PLOS ONE, Public Library of Science, vol. 3(7), pages 1-12, July.
    2. Avner Schlessinger & Marco Punta & Guy Yachdav & Laszlo Kajan & Burkhard Rost, 2009. "Improved Disorder Prediction by Combination of Orthogonal Approaches," PLOS ONE, Public Library of Science, vol. 4(2), pages 1-10, February.
    3. Sitao Wu & Yang Zhang, 2008. "ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction," PLOS ONE, Public Library of Science, vol. 3(10), pages 1-8, October.
    4. Yanay Ofran & Burkhard Rost, 2007. "Protein–Protein Interaction Hotspots Carved into Sequences," PLOS Computational Biology, Public Library of Science, vol. 3(7), pages 1-8, July.
    5. Avner Schlessinger & Jinfeng Liu & Burkhard Rost, 2007. "Natively Unstructured Loops Differ from Other Loops," PLOS Computational Biology, Public Library of Science, vol. 3(7), pages 1-12, July.
    6. Jiangning Song & Hao Tan & Khalid Mahmood & Ruby H P Law & Ashley M Buckle & Geoffrey I Webb & Tatsuya Akutsu & James C Whisstock, 2009. "Prodepth: Predict Residue Depth by Support Vector Regression Approach from Protein Sequences Only," PLOS ONE, Public Library of Science, vol. 4(9), pages 1-14, September.
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    Cited by:

    1. Cheng Zheng & Mingjun Wang & Kazuhiro Takemoto & Tatsuya Akutsu & Ziding Zhang & Jiangning Song, 2012. "An Integrative Computational Framework Based on a Two-Step Random Forest Algorithm Improves Prediction of Zinc-Binding Sites in Proteins," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-15, November.
    2. Ivan Kalev & Michael Habeck, 2013. "Confidence-Guided Local Structure Prediction with HHfrag," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-7, October.
    3. Harinder Singh & Sandeep Singh & Gajendra P S Raghava, 2014. "Evaluation of Protein Dihedral Angle Prediction Methods," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-9, August.

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