Identification of biomolecular conformations from incomplete torsion angle observations by hidden Markov models.
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.
Institute of Mathematics II, Free University Berlin, Arnimallee 2-6, 14195 Berlin, Germany.
Alexander
A
Fischer
Sonja
S
Waldhausen
Illia
I
Horenko
Eike
E
Meerbach
Christof
C
Schütte
D000409:Alanine / Q000737:chemistry; D000465:Algorithms; D004247:DNA / Q000737:chemistry; D008390:Markov Chains; D008958:Models, Molecular; D008968:Molecular Conformation; D009010:Monte Carlo Method; D010455:Peptides / Q000737:chemistry; D018583:Pliability; D011336:Probability
D010455:Peptides; D004247:DNA; D000409:Alanine