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Protein fold determination from sparse distance restraints: The restrained generic protein direct Monte Carlo method

Journal Article · · Journal of Physical Chemistry B: Materials, Surfaces, Interfaces, amp Biophysical
DOI:https://doi.org/10.1021/jp983429+· OSTI ID:347534
; ; ;  [1];  [2]
  1. California Inst. of Tech., Pasadena, CA (United States)
  2. Teijin Limited, Iwakuni, Yamaguchi (Japan). Polymer and Materials Research Labs.
The authors present the generate-and-select hierarchy for tertiary protein structure prediction. The foundation of this hierarchy is the Restrained Generic Protein (RGP) Direct Monte Carlo method. The RGP method is a highly efficient off-lattice residue buildup procedure that can quickly generate the complete set of topologies that satisfy a very small number of interresidue distance restraints. For three restraints uniformly distributed in a 72-residue protein, the authors demonstrate that the size of this set is {approximately}10{sup 4}. The RGP method can generate this set of structures in less than 1 h using a Silicon Graphics R10000 single processor workstation. Following structure generation, a simple criterion that measures the burial of hydrophobic and hydrophilic residues can reliably select a reduced set of {approximately}10{sup 2} structures that contains the native topology. A minimization of the structures in the reduced set typically ranks the native topology in the five lowest energy folds. Thus, using this hierarchical approach, the authors suggest that de novo prediction of moderate resolution globular protein structure can be achieved in just a few hours on a single processor workstation.
Sponsoring Organization:
USDOE, Washington, DC (United States)
OSTI ID:
347534
Journal Information:
Journal of Physical Chemistry B: Materials, Surfaces, Interfaces, amp Biophysical, Journal Name: Journal of Physical Chemistry B: Materials, Surfaces, Interfaces, amp Biophysical Journal Issue: 15 Vol. 103; ISSN 1089-5647; ISSN JPCBFK
Country of Publication:
United States
Language:
English