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Identification of biomolecular conformations from incomplete torsion angle observations by hidden markov models
Author(s) -
Fischer Alexander,
Waldhausen Sonja,
Horenko Illia,
Meerbach Eike,
Schütte Christof
Publication year - 2007
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20692
Subject(s) - statistical physics , monte carlo method , torsion (gastropod) , markov chain monte carlo , markov chain , von mises distribution , series (stratigraphy) , hidden markov model , algorithm , von mises yield criterion , hybrid monte carlo , molecular dynamics , computer science , biological system , physics , mathematics , computational chemistry , chemistry , artificial intelligence , machine learning , thermodynamics , finite element method , biology , zoology , paleontology , statistics
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.