期刊
JOURNAL OF COMPUTATIONAL CHEMISTRY
卷 28, 期 15, 页码 2453-2464出版社
WILEY
DOI: 10.1002/jcc.20692
关键词
aggregation; angular data; biomolecules; EM algorithm; HMM; metastability; torsion angle; Viterbi algorithm; von Mises distribution
We present a novel method for the identification of the most important conformations of a biomolecular system from molecular dynamics or Metropolis Monte Carlo time series by means of Hidden Markov Models (HMMs). We show that identification is possible based on the observation sequences of some essential torsion or backbone angles. In particular, the method still provides good results even if the conformations do have a strong overlap in these angles. To apply HMMs to angular data, we use von Mises output distributions. The performance of the resulting method is illustrated by numerical tests and by application to a hybrid Monte Carlo time series of trialanine and to MD simulation results of a DNA-oligomer. (c) 2007 Wiley Periodicals, Inc.
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