Multiscale Modelling in Molecular Dynamics: Biomolecular Conformations as Metastable States

We report on a novel approach to the automatic identification of metastable states from long term simulation of complex molecular systems. The new approach is based on a hierarchical concept of metastability: metastable states are understood as subsets of state or configuration space from which the dynamics exits only very rarely; subsets with the smallest exit probabilities are of most interest, their further decomposition then may reveal subsets from which exiting is less but comparably difficult for the system under investigation. The article gives a survey of the theoretical foundation of the approach and its algorithmic realization that generalizes the well-known concept of Hidden Markov Models. The performance of the resulting algorithm are illustrated by application to a 100 ns simulation of penta-alanine with explicit water. We demonstrate the resulting metastable states allow to reveal the conformation dynamics of the moelcule.

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