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Related Experiment Videos

A branch and bound algorithm for protein structure refinement from sparse NMR data sets

D M Standley1, V A Eyrich, A K Felts

  • 1Department of Chemistry and Center for Biomolecular Simulation, Columbia University, New York, NY, 10027, USA.

Journal of Molecular Biology
|January 26, 1999
PubMed
Summary

New computational methods improve low-resolution protein structure prediction using nuclear magnetic resonance (NMR) constraints and a novel global optimization approach. This protein structure prediction technique offers higher accuracy than standard methods.

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Area of Science:

  • Structural biology
  • Computational chemistry
  • Biophysics

Background:

  • Predicting protein tertiary structures is crucial for understanding biological function.
  • Nuclear magnetic resonance (NMR) spectroscopy provides distance constraints for structure determination.
  • Existing computational methods like distance geometry/molecular dynamics (DGMD) have limitations in accuracy and efficiency.

Purpose of the Study:

  • To develop and assess new computational methods for predicting protein tertiary structures to low resolution.
  • To improve the accuracy and efficiency of protein structure prediction using NMR data.
  • To compare the performance of the new methods against established protocols like X-PLOR.

Main Methods:

  • Utilizing secondary structure information and limited long-range NMR distance constraints.

Related Experiment Videos

  • Employing a modified alphaBB (branch and bound) global optimization algorithm.
  • Minimizing an objective function integrating NMR restraints with a protein folding potential (hydrophobicity, excluded volume, van der Waals interactions).

    • Main Results:

    • The new methods achieved substantial improvements in root-mean-square deviation (RMSD) from native structures compared to DGMD.
    • DGMD calculations often produced qualitatively erroneous and systematically non-compact structures.
    • The developed approach uniformly generated high-quality, low-resolution structures for proteins up to 183 residues with modest computational effort.

    Conclusions:

    • The new computational/NMR protocol offers a significant advancement in protein structure prediction.
    • This methodology provides higher accuracy and reliability than standard DGMD approaches.
    • The results encourage the development of protocols for accelerating structure determination in larger, potentially underconstrained proteins.