Author ORCID Identifier

0000-0002-6461-0030

Date of Award

Fall 12-13-2021

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Chemistry

First Advisor

Donald Hamelberg

Abstract

A deep understanding of the role of motions in the functional mechanisms of biomolecules can potentially speed up molecular dynamics (MD) simulations of the interaction of the same drug target with numerous drug candidates by reusing the simulation of the drug target. This deep understanding can also enable using dynamics as an additional tool to steer a drug toward its intended target and avoid side effects. To gain this understanding, we have modeled the internal motions, the structural hierarchy, and the dynamical hierarchy of biomolecules by analyzing MD simulations. We find that the diffusion coefficients of the internal motions of some biomolecules vary over time and have spectra with significant peaks. We mine and correlate the occurrence of frequent substructures in the major conformations of a protein and avoid the combinatorial explosion of configurations in the analysis. We model the contributions of residues to the coupled Markovian dynamics of multiple domains of a protein. The model discovers residue states that act as indicators of the domain states, and one domain acts as a switch that controls the dynamics of the other domains. Collectively, our results enable a hierarchical decomposition of the motion of biomolecules, extract insights of their functional mechanisms from MD simulations, and provide a foundation for designing architectures of artificial neural networks that extract dynamical and hierarchical insights from MD simulations.

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