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Title: Markov dynamic models for long-timescale protein motion
Keywords: Markov Dynamic Model, Long-Timescale, Protein Motion
Issue Date: 23-Dec-2011
Citation: CHIANG TSUNG HAN (2011-12-23). Markov dynamic models for long-timescale protein motion. ScholarBank@NUS Repository.
Abstract: Molecular Dynamics (MD) simulation is a well-established method used for studying protein motion at the atomic scale. However, it is computationally intensive and generates massive amounts of data. One way of addressing the dual challenges of computation efficiency and data analysis is to construct simplified models of long-timescale protein motion from MD simulation data. This thesis proposes the use of Markov Dynamic Models (MDMs) for the modeling of long-timescale protein motion. In a MDM, each state represents a probabilistic distribution of a protein?s 3-D structure, and the transitions between states represent the change of conformation over time, i.e. motion. Therefore, the dynamics of protein motion can be intuitively analyzed from the explicit graphical representation of a MDM. A principled criterion is also proposed for evaluating the quality of a model by its ability to predict simulation trajectories. This allows the most suitable model complexity to be determined, and addresses a main shortcoming of existing methods. In addition, equations are derived to compute ensemble properties of protein motion. This crucially allows MDMs to be validated against wet lab experiments. Experimental results on the alanine dipeptide and the villin headpiece proteins are consistent with current biological knowledge, and demonstrate the usefulness of MDMs in practical use.
Appears in Collections:Ph.D Theses (Open)

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