Leung, Jeremy M. G.
(2024)
Paving the Way for Protein (Un)Folding Pathways: Towards Machine-Learning Guided Weighted Ensemble Simulations of Rare-Event Sampling.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Molecular dynamics simulations of rare events such as protein folding and unfolding are difficult due the large differences in time scales between the process of interest and stable states. In my dissertation, I will present advances to the weighted ensemble (WE) path sampling strategy that I have developed and demonstrate how the WE strategy can be used to efficiently simulate a protein-folding process. In Chapter 1, I motivate the need for studying biological processes using molecular dynamics simulations, introduce the WE method, and present potential future directions. In Chapters 2 and 3, I will detail joint experimental-simulation studies in which I have characterized both the thermodynamics and kinetics of a folding process for a small alpha-helical protein and five artificially modified variants of this protein. In the second half of my thesis, I will further describe some of the improvements to the WE strategy that allow for simulation of long time scale processes. In Chapter 4, I will detail changes in the open-source WESTPA software package for running WE simulations. In Chapter 5, I will present how machine learning-based progress coordinates can be used to accelerate WE simulation sampling on-the-fly. These chapters, together, demonstrate the current capabilities of the WE strategy, including enhancements provided by machine learning.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
20 December 2024 |
| Date Type: |
Publication |
| Defense Date: |
1 August 2024 |
| Approval Date: |
20 December 2024 |
| Submission Date: |
17 September 2024 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
204 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Dietrich School of Arts and Sciences > Chemistry |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
molecular dynamics, weighted ensemble, markov state model, biophysics, proteins, biophysical chemistry |
| Date Deposited: |
20 Dec 2024 13:43 |
| Last Modified: |
20 Dec 2024 13:43 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/46957 |
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