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Evaluation of Protein Dihedral Angle Prediction Methods

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  • Harinder Singh
  • Sandeep Singh
  • Gajendra P S Raghava

Abstract

Tertiary structure prediction of a protein from its amino acid sequence is one of the major challenges in the field of bioinformatics. Hierarchical approach is one of the persuasive techniques used for predicting protein tertiary structure, especially in the absence of homologous protein structures. In hierarchical approach, intermediate states are predicted like secondary structure, dihedral angles, Cα-Cα distance bounds, etc. These intermediate states are used to restraint the protein backbone and assist its correct folding. In the recent years, several methods have been developed for predicting dihedral angles of a protein, but it is difficult to conclude which method is better than others. In this study, we benchmarked the performance of dihedral prediction methods ANGLOR and SPINE X on various datasets, including independent datasets. TANGLE dihedral prediction method was not benchmarked (due to unavailability of its standalone) and was compared with SPINE X and ANGLOR on only ANGLOR dataset on which TANGLE has reported its results. It was observed that SPINE X performed better than ANGLOR and TANGLE, especially in case of prediction of dihedral angles of glycine and proline residues. The analysis suggested that angle shifting was the foremost reason of better performance of SPINE X. We further evaluated the performance of the methods on independent ccPDB30 dataset and observed that SPINE X performed better than ANGLOR.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0105667
    DOI: 10.1371/journal.pone.0105667
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    References listed on IDEAS

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    1. 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.
    2. Bent Petersen & Claus Lundegaard & Thomas Nordahl Petersen, 2010. "NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-9, November.
    3. Hothorn, Torsten & Hornik, Kurt & van de Wiel, Mark A. & Zeileis, Achim, 2008. "Implementing a Class of Permutation Tests: The coin Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i08).
    4. 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.
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