IIIT Hyderabad Publications |
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“A probabilistic framework for constructing temporal relations in Monte Carlo trajectoriesAuthor: aditya.c Date: 2018-05-31 Report no: IIIT/TH/2018/20 Advisor:Deva U Priyakumar AbstractKnowledge of the structure and dynamics of biomolecules is essential for elucidating the underlying mechanisms of biological processes. Given the stochastic nature of many biological processes, like protein unfolding, it’s almost impossible that two independent simulations will generate the exact same sequence of events, which makes direct analysis of simulations difficult. Statistical models like Markov Chains, transition networks etc. help in shedding some light on the mechanistic nature of such processes by predicting long-time dynamics of these systems from short simulations. However, such methods fall short in analyzing trajectories which are oblivious to temporal information, for example, Monte Carlo simulation methods. In this thesis we propose a probabilistic algorithm, borrowing concepts from graph theory and statistical mixture models, to extract folding pathways from molecular dynamic trajectories. A suitable vector representation was chosen to represent each frame in the macromolecular trajectory (as a series of interaction and conformational energies) and dimensionality reduction was performed using principal component analysis (PCA). The trajectory was then clustered using a density-based clustering algorithm, where each cluster represents a metastable state on the potential energy surface (PES) of the biomolecule under study. A graph was created with these clusters as nodes and the most probable path of (un)folding from an initial folded state to a final unfolded state is conceived as the widest path along this graph. We have tested our method on RNA hairpin unfolding trajectory in aqueous urea solution. Our method makes the understanding of the mechanism of unfolding in RNA hairpin molecule more tractable. As this method doesn’t rely on temporal data it can be used to analyze trajectories from Monte Carlo sampling techniques and replica exchange molecular dynamics (REMD). This work is an effort in the direction towards harnessing the full potential of enhanced sampling methods, which until now, is mostly limited to evaluating equilibrium properties of the system Full thesis: pdf Centre for Computational Natural Sciences and Bioinformatics |
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