Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/33297
Title: Markov dynamic models for long-timescale protein motion
Authors: CHIANG TSUNG HAN
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.
URI: http://scholarbank.nus.edu.sg/handle/10635/33297
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Chiang-MDM-Protein-Motion.pdf14.67 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

178
checked on Nov 24, 2018

Download(s)

107
checked on Nov 24, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.