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Integrating EPR and computational modeling to measure protein structure and dynamics

Bogetti, Xiaowei (2024) Integrating EPR and computational modeling to measure protein structure and dynamics. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

Electron paramagnetic resonance (EPR) spectroscopy coupled with site-directed spin labeling has become a powerful method for probing conformational diversity and dynamics of macromolecules. The sparse distance and dynamics information obtained from EPR measurements greatly benefit from computational modeling. In Chapter 1 of this dissertation, I provide a comprehensive overview of different modeling techniques that can be coupled with EPR. These computational approaches can be used to sample protein and label conformations, simulate EPR spectra, predict or refine protein structures and capture large-amplitude conformational transitions. In Chapter 2, I describe the development of new force field parameters for double histidine- copper(II) (dHis)-Cu(II)-based EPR labels. Molecular dynamics (MD) simulations based on these new force fields generate distance distributions between the labels in remarkable agreement with experiments. These MD-trajectories help us understand the orientational selectivity in double electron-electron resonance (DEER) using Cu(II)-based labels. In Chapter 3, I showcase a new strategy that enables sampling conformational changes at atomic resolution by combining dHis- Cu(II) EPR and weighted ensemble MD simulations. This strategy has been applied to sample a seconds-timescale conformational change in the homodimeric detoxification enzyme. These simulations reveal the negative cooperativity within the enzyme controlled by key residue-residue interactions, which may be essential for the enzyme to protect cells from a broad range of toxins. In Chapter 4, I discuss the development and application of an in silico approach to optimize DEER data acquisition in collaboration with my coworker. This optimal DEER acquisition scheme improves the efficiency of obtaining Cu(II)-based EPR distance distributions, and reduces the data collection time by as much as six fold. Overall, this body of work presents the potential of integrating EPR measurements and computational modeling to tackle various biophysical questions over a wide range of timescales.

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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Bogetti, Xiaoweixid37@pitt.eduxid37
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSaxena, Sunilsksaxena@pitt.edusksaxena0000-0001-9098-6114
Committee MemberHorne, Sethhorne@pitt.edu
Committee MemberLaaser, Jenniferj.laaser@pitt.edu
Committee MemberWang, JunmerJUW79@pitt.eduJUW79
Date: 8 January 2024
Date Type: Publication
Defense Date: 15 September 2023
Approval Date: 8 January 2024
Submission Date: 21 September 2023
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 177
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: Electron paramagnetic resonance; computational modeling; force field; molecular dynamics; weighted ensemble; protein conformations and dynamics.
Date Deposited: 08 Jan 2024 18:20
Last Modified: 08 Jan 2024 18:20
URI: http://d-scholarship.pitt.edu/id/eprint/45411

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