Premium
Hybrid Monte Carlo with adaptive temperature in mixed‐canonical ensemble: Efficient conformational analysis of RNA
Author(s) -
Fischer Alexander,
Cordes Frank,
Schütte Christof
Publication year - 1998
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/(sici)1096-987x(19981130)19:15<1689::aid-jcc2>3.0.co;2-j
Subject(s) - canonical ensemble , markov chain monte carlo , monte carlo method , statistical physics , umbrella sampling , gibbs sampling , sampling (signal processing) , grand canonical ensemble , metropolis–hastings algorithm , molecular dynamics , hybrid monte carlo , chemistry , computer science , physics , mathematics , computational chemistry , bayesian probability , statistics , artificial intelligence , filter (signal processing) , computer vision
A hybrid Monte Carlo method with adaptive temperature choice is presented that exactly generates the distribution of a mixed‐canonical ensemble composed of two canonical ensembles at low and high temperature. The analysis of resulting Markov chains with the reweighting technique shows an efficient sampling of the canonical distribution at low temperature whereas the high temperature component facilitates conformational transitions, which allows shorter simulation times. The algorithm is tested by comparing analytical and numerical results for the small n ‐butane molecule before simulations are performed for a triribonucleotide. Sampling the complex multiminima energy landscape of this small RNA segment, we observe enforced crossing of energy barriers. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1689–1697, 1998