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ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction

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  • Sitao Wu
  • Yang Zhang

Abstract

We developed a composite machine-learning based algorithm, called ANGLOR, to predict real-value protein backbone torsion angles from amino acid sequences. The input features of ANGLOR include sequence profiles, predicted secondary structure and solvent accessibility. In a large-scale benchmarking test, the mean absolute error (MAE) of the phi/psi prediction is 28°/46°, which is ∼10% lower than that generated by software in literature. The prediction is statistically different from a random predictor (or a purely secondary-structure-based predictor) with p-value

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0003400
    DOI: 10.1371/journal.pone.0003400
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Ngaam J Cheung & Wookyung Yu, 2018. "De novo protein structure prediction using ultra-fast molecular dynamics simulation," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-17, November.

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