DOI

10.17077/etd.dm5i-4kk5

Document Type

Dissertation

Date of Degree

Spring 2019

Access Restrictions

Access restricted until 07/29/2020

Degree Name

PhD (Doctor of Philosophy)

Degree In

Biochemistry

First Advisor

DeMali, Kris A.

First Committee Member

Elcock, Adrian H.

Second Committee Member

Margulis, Claudio J.

Third Committee Member

Spies, Michael A.

Fourth Committee Member

Washington, Michael T.

Abstract

Biophysical simulation can be an excellent complement to experimental techniques, but there are unresolved practical constraints to simulation. While computers have continued to improve, the scale of systems we wish to study has continued to increase. This has driven the use of approximate energy functions (force fields), compensating for relatively short simulations via careful structure preparation and accelerated sampling techniques. To address structure preparation, we developed the many-body dead end elimination (MB-DEE) optimizer. We first proved the MB-DEE algorithm on a set of PCNA crystal structures, and accelerated it on GPUs to optimize 472 homology models of proteins implicated in inherited deafness. Advanced physics has been clearly demonstrated to help optimize structures, and with GPU acceleration, this becomes a possibility for large numbers of structures. We also show the novel “simultaneous bookending” algorithm, which is a new approach to indirect free energy (IFE) methods. These first perform simulations under a cheaper “reference” potential, then correct the thermodynamics to a more sophisticated “target” potential, combining the speed of the reference potential with the accuracy of the target potential. Simultaneous bookending is shown as a valid IFE approach, and methods to realize speedups vs. the direct path are discussed. Finally, we are developing the Monte Carlo Orthogonal Space Random Walk (MC-OSRW) algorithm for high-performance alchemical free energy simulations, bypassing some of the difficulty in OSRW methods. This work helps prevent inaccuracies caused by simpler electrostatic models by making advanced polarizable force fields more accessible for routine simulation.

Keywords

Alchemistry, Enhanced Sampling, GPU Acceleration, Molecular Dynamics, Optimization Techniques, Structural Biology

Pages

xix, 196 pages

Bibliography

Includes bibliographical references (pages 147-159).

Comments

This thesis has been optimized for improved web viewing. If you require the original version, contact the University Archives at the University of Iowa: https://www.lib.uiowa.edu/sc/contact/.

Copyright

Copyright © 2019 Jacob Mordechai Litman

Available for download on Wednesday, July 29, 2020

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