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A Protocol for Computer-Based Protein Structure and Function Prediction
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Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: assessment

S Ołdziej1, C Czaplewski, A Liwo

  • 1Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA.

Proceedings of the National Academy of Sciences of the United States of America
|May 17, 2005
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We improved a physics-based protein structure prediction method using conformational space annealing. This method accurately predicts protein structures, including large alpha+beta proteins, advancing computational structural biology.

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Biophysics

Background:

  • Protein structure prediction is crucial for understanding biological function.
  • Physics-based methods offer a promising avenue for accurate structure determination.
  • Previous methods faced challenges with topological accuracy, especially for alpha+beta proteins.

Purpose of the Study:

  • To describe recent improvements in a physics-based protein structure prediction method.
  • To evaluate the enhanced method's performance in blind tests of protein structure prediction.
  • To demonstrate progress in accurately predicting protein structures from amino acid sequences.

Main Methods:

  • Utilizing a physics-based UNRES energy function for conformational space search.
  • Employing conformational space annealing for global optimization.
  • Converting coarse-grained structures to all-atom representation and energy minimization using ECEPP/3 force field.

Main Results:

  • Successful prediction of large protein fragments (60-70 residues) with C(alpha) rmsd <6 A in the first blind test.
  • Accurate prediction of whole protein structures (53-235 residues) in the second blind test, including a 102-residue alpha+beta protein (TM0487) with 7.3-A C(alpha) rmsd.
  • Correct prediction of the topology for a 235-residue alpha-helical protein (PhoU) within 8 A rmsd.

Conclusions:

  • The improved physics-based method shows significant progress in protein structure prediction accuracy.
  • The method demonstrates capability in predicting complex protein topologies, including large alpha+beta proteins.
  • This work advances the field of ab initio protein structure prediction using computational approaches.