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Building proteins from C alpha coordinates using the dihedral probability grid Monte Carlo method

A M Mathiowetz1, W A Goddard

  • 1Materials and Molecular Simulation Center, Beckman Institute (139-74), California Institute of Technology, Pasadena 91125, USA.

Protein Science : a Publication of the Protein Society
|June 1, 1995
PubMed
Summary

Dihedral probability grid Monte Carlo (DPG-MC) accurately predicts complete protein structures from C alpha coordinates. This method guides conformational searches, achieving high accuracy for backbone and side chain modeling in proteins.

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Biophysics

Background:

  • Conformational sampling is crucial for peptide and protein modeling.
  • Predicting complete protein structures from limited data presents significant challenges.

Purpose of the Study:

  • To present the Dihedral Probability Grid Monte Carlo (DPG-MC) method.
  • To apply DPG-MC for predicting complete protein structures from C alpha coordinates.
  • To demonstrate DPG-MC's utility in systems with geometric constraints.

Main Methods:

  • Utilized Dihedral Probability Grid Monte Carlo (DPG-MC) for conformational sampling.
  • Employed residue-specific conformational propensities to guide protein building from N-terminus to C-terminus.
  • Applied the method to predict complete protein structures using C alpha coordinates.

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Main Results:

  • DPG-MC accurately modeled both backbone and side chains of proteins.
  • Backbone atoms were predicted with root-mean-square (RMS) errors of approximately 0.5 Å compared to X-ray crystal structures.
  • All atoms were predicted with an RMS error of 1.7 Å or better.

Conclusions:

  • DPG-MC is a versatile method for general-purpose conformational sampling in protein modeling.
  • The method accurately predicts complete protein structures, including backbone and side chains.
  • DPG-MC is effective for applications like homology modeling and fitting structures to crystallographic data.