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A Protocol for Computer-Based Protein Structure and Function Prediction
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Published on: November 3, 2011

TANGLE: two-level support vector regression approach for protein backbone torsion angle prediction from primary

Jiangning Song1, Hao Tan, Mingjun Wang

  • 1Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, Victoria, Australia. Jiangning.Song@monash.edu

Plos One
|February 10, 2012
PubMed
Summary

TANGLE accurately predicts protein backbone torsion angles (Phi and Psi) from amino acid sequences. This new method improves upon existing tools, aiding in protein structure prediction and fold recognition.

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Area of Science:

  • Structural Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Protein backbone geometry is defined by torsion angles (Phi and Psi) around specific bonds.
  • Accurate prediction of these angles from amino acid sequences is crucial for protein structure prediction.

Purpose of the Study:

  • To develop a novel computational approach, TANGLE (Torsion ANGLE predictor), for predicting protein backbone torsion angles directly from amino acid sequences.
  • To evaluate the performance of TANGLE against existing state-of-the-art methods and random prediction.

Main Methods:

  • TANGLE employs a two-level support vector regression model.
  • It utilizes diverse sequence-derived features, including evolutionary profiles (position-specific scoring matrices), predicted secondary structure, solvent accessibility, and disordered regions.

Main Results:

  • TANGLE achieved mean absolute errors of 27.8° for Phi angles and 44.6° for Psi angles on a benchmark dataset.
  • These results represent a 1% and 3% improvement over the ANGLOR prediction tool, respectively.
  • TANGLE significantly outperformed random prediction methods (p-value < 1.46e-147 for Phi, p-value < 7.97e-150 for Psi).

Conclusions:

  • TANGLE offers a complementary and effective approach to existing torsion angle prediction algorithms.
  • The predicted torsion angles can serve as valuable restraints for protein structure prediction and fold recognition.
  • TANGLE is publicly accessible for researchers to utilize in their studies.