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A number of modern sampling methods probe long time behavior in complexbiomolecules using a set of relatively short trajectory segments. Markov statemodels (MSMs) can be useful in analyzing such data sets, but in particularlycomplex landscapes, the available trajectory data may prove insufficient forconstructing valid Markov models. Here, we explore the potential utility ofhistory-dependent analyses applied to relatively poor decompositions ofconfiguration space for which MSMs are inadequate. Our approaches build onprevious work [Suarez et. al., JCTC 2014] showing that, with sufficient historyinformation, unbiased equilibrium and non-equilibrium observables can beobtained even for arbitrary non-Markovian divisions of phase space. We explorea range of non-Markovian approximations using varying amounts of historyinformation to model the finite length of trajectory segments, applying theanalyses to toy models as well as several proteins previously studied bymicrosec-milisec scale atomistic simulations [Lindorff-Larsen et. al., Science2011].

Learning from history: Non-Markovian analyses of complex trajectories for extracting long-time behavior