USING MARKOV MODELS TO SIMULATE ESR SPECTRA FROM MD TRAJECTORIES

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Simulating electron spin resonance (ESR) spectra directly from molecular dynamics simulations of a spin labeled protein necessitates a large number(hundreds or thousands) of relatively long (hundreds of ns) trajectories. To meet this challenge, we explored the possibility of constructing accurate stochastic models of the spin label dynamics from atomistic trajectories. A systematic, two-step procedure, based on the probabilistic framework of hidden Markov models,is developed to build a discrete-time Markov chain process that faithfully captures the internal spin label dynamics on time scales longer than about 150 ps. The constructed Markov model was used both to gain insight into the long-lived conformations of the spin label and to generate the stochastic trajectories required for the simulation of ESR spectra. The methodology has been illustrated with an application to the case of a spin labeled poly-alanine alpha helix in explicit solvent.