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Paving the Way for Protein (Un)Folding Pathways: Towards Machine-Learning Guided Weighted Ensemble Simulations of Rare-Event Sampling

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 (Unpublished)

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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Status: Unpublished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:
CreatorsEmailPitt UsernameORCID
Leung, Jeremy M. G.jml230@pitt.edujml2300000-0001-7021-4619
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChong, Lillian T.ltchong@pitt.edultchong0000-0002-0590-483X
Committee MemberCoalson, Rob. D.coalson@pitt.educoalson0000-0003-0201-0176
Committee MemberHorne, W. Sethhorne@pitt.eduhorne0000-0003-2927-1739
Committee MemberOas, Terrence G.oas@duke.edu0000-0002-3067-2743
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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