Computer Science ETDs

Publication Date

Fall 11-15-2018

Abstract

Simulation of large molecular structures and their interactions has become a major component of modern biomolecular research. Methods to simulate these type of molecules span a wide array of resolutions, from all atom molecular dynamics to model interaction energetics to systems of linear equations to evaluate population kinetics. In recent years, there has been an acceleration of molecular structural information production, primarily from x-ray crystallography and electron microscopy. This data has provided modelers the ability to produce better representations of these molecular structures. The purpose of this research is to take advantage of this information to develop multi-resolution models for the analysis of large molecule motions and interactions. Our methodology focuses on the use of structural models of a given biological system and simulating the molecules using different conditions (number or ratio of molecules being simulated) and constraints (rigid or semi-flexible models). We combine computational geometry and statistical techniques to perform efficient structural modeling and simulation. Our goal is to utilize our methods to analyze the effect of geometry on molecule interactions, e.g., shape of packed structures or influences of steric hindrance caused by interacting molecules. We focus our work on larger molecular systems, both in size of the molecular structures and number of interacting molecules. The focus of our evaluation is the human allergic immune response. The immune response is triggered by cell surface molecular aggregation of antibodies via an antigen. With our analysis we gain insight into how different allergen geometries affect the size and shape of aggregate structures that form on the cell surface. We perform a multi-resolution analysis of our structures and model the problem in two ways, a lower resolution rigid body representation which can model the aggregation process, and a higher resolution flexible model which can be used to fit structural experimental data. In the lower resolution work, we develop methods to geometrically model, simulate and analyze antibody aggregation. We show our technique handles the large size and number of molecules involved in aggregation, and we study the impact of model resolution on simulations of geometric structures. In the higher resolution work, we introduce methods to model and fit molecular structures into electron microscopy datasets (20A - 40A resolution). We use Gaussian mixture models to describe molecular systems with high flexibility thus enabling the generation of conformations that fit an input tomographic tilt series, a set of 2D images of a 3D molecule taken at a variety of angles. We also apply our method to experimental data, fitting a structure imaged using cryo electron microscopy tomography.

Language

English

Keywords

Coarse Grained Modeling, Computational Biology, Structural Biology, Cryo EM

Document Type

Dissertation

Degree Name

Computer Science

Level of Degree

Doctoral

Department Name

Department of Computer Science

First Committee Member (Chair)

Lydia Tapia

Second Committee Member

Bruna Jacobson

Third Committee Member

Shuang Luan

Fourth Committee Member

Darko Stefanovic

Fifth Committee Member

Bridget Wilson

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