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A knowledge-based method for protein structure refinement and prediction

S Subramaniam1, D K Tcheng, J M Fenton

  • 1Beckman Institute, National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign 61801, USA.

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|January 1, 1996
PubMed
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This study introduces a knowledge-based method using statistical potentials derived from protein structures to predict and assess protein conformations. The approach accurately refines noisy structures into native-like forms, aiding in protein structure determination.

Area of Science:

  • Structural biology
  • Computational biology
  • Biophysics

Background:

  • Protein native conformation is governed by interatomic interactions encoded in the amino acid sequence.
  • Accurate protein structure determination is crucial for understanding biological function.

Purpose of the Study:

  • To develop and validate a knowledge-based approach for protein structure assessment and prediction.
  • To derive statistical potentials describing interatomic interactions in native proteins.

Main Methods:

  • Utilized a set of high-resolution protein structures to derive atom-pairwise distance probability density functions as statistical potentials.
  • Applied these potentials to assess and refine randomized and noisy protein structures.

Main Results:

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  • Achieved highly precise (< or = 2A) native-like structures from noisy inputs across various protein sizes and topologies.
  • Demonstrated sensitivity to subtle structural distortions and consistent optimization towards higher probability structures.

Conclusions:

  • The derived statistical potentials offer a powerful tool for protein structure refinement.
  • This method enhances the precision of X-ray and Nuclear Magnetic Resonance (NMR) derived structures.