(38) Simon Sherman, Stanley L. Sclove, Oleg Shats and Leonid
Kirnarsky.
Dihedral Probability Cluster Monte Carlo Procedure for
Conformational Analysis of Proteins.
*Internet Journal of Chemistry, Vol. 1 (1998), Article 106,*
8 pp.
URL: http://www.ijc.com/articles/1998v1/106.

A mixture-model cluster analysis of dihedral angle distributions
observed in high-resolution proteins from the Protein Data Bank
has shown that conformation of the amino acid residues are very
highly
clustered in the space of the (phi, psi, chi1) dihedral angles.
Based on these results, an effective new procedure (called DPC-MC,
dihedral probability cluster Monte Carlo) for sampling the
conformational space of proteins was developed. DPC-MC uses the
cluster probabilities (relative frequencies) to prioritize the
sampling of conformational space. Means and standard deviations
for the torsion angles are used in this procedure to estimate
values of the angles and the angular ranges. DPC-MC may be
incorporated into any protocol for protein structure determination
and analysis using values and/or allowed ranges for dihedral angles
as input data.
The procedure allows one to generate "good" intial conformations
of proteins in contrast to randomly chosen ones. DPC-MC can be
beneficial for three-dimensional protein structure determination
from NMR data, building protein conformations from
C-alpha-atom traces, homology modeling of proteins, *de novo*
predicting of polypeptide conformations, *etc.*
The benefits of using the DPC-MC procedure were demonstrated by
reconstructing
complete three-dimensional structures of a protein, flavodoxin,
from its C-alpha-atomic coordinates.

KEY WORDS: Monte Carlo, Markov chain, protein structure