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Using imperfect secondary structure predictions to improve molecular structure computations.

C C Chen1, J P Singh, R B Altman

  • 1Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA.

Bioinformatics (Oxford, England)
|March 9, 1999
PubMed
Summary
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Imperfect protein secondary structure predictions can significantly improve 3D structure computations. Using disjunctive constraints, even low-accuracy predictions yield robust results, nearly halving RMS error.

Area of Science:

  • Computational biology
  • Structural bioinformatics

Background:

  • Protein structure estimation relies on integrating diverse data sources.
  • Secondary structure predictions, though imperfect (approx. 70% accuracy), offer valuable structural insights.
  • Algorithms incorporating secondary structure data require robust handling of prediction inaccuracies.

Purpose of the Study:

  • To develop and test a method for incorporating imperfect secondary structure predictions into protein structure computations.
  • To evaluate the effectiveness of 'disjunctive' constraints for handling probabilistic structural information.

Main Methods:

  • Modified a probabilistic least squares algorithm to accept 'disjunctive' constraints, assigning probabilities to predicted secondary structure types (helix, strand, coil).
  • Tested various weighting schemes for disjunctive constraints against a synthetic dataset.

Related Experiment Videos

  • Compared performance with methods not using disjunctive constraints.

    • Main Results:

    • Directly using predictions as 100% accurate led to poor structural quality.
    • Disjunctive constraint interpretations proved robust, significantly enhancing computed structure quality.
    • Even low-accuracy predictions (58% correct) nearly halved the root-mean-square (RMS) error.

    Conclusions:

    • Imperfect secondary structure predictions can substantially improve 3D protein structure calculations.
    • Appropriate interpretation of predictions, particularly using disjunctive constraints, maximizes their utility.
    • The method demonstrates that imperfect data, when handled correctly, can be nearly as beneficial as perfect predictions.