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Long-Timescale Kinetics and Mechanisms of Protein Conformational Change
Long-Timescale Kinetics and Mechanisms of Protein Conformational Change
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105115
- ISBN
- 9798293818969
- DDC
- 540
- 저자명
- Guo, Spencer C.
- 서명/저자
- Long-Timescale Kinetics and Mechanisms of Protein Conformational Change
- 발행사항
- [Sl] : The University of Chicago, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 276 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-03, Section: B.
- 주기사항
- Advisor: Dinner, Aaron R.;Roux, Benoit.
- 학위논문주기
- Thesis (Ph.D.)--The University of Chicago, 2025.
- 초록/해제
- 요약Because biomolecules are complex, high-dimensional systems exhibiting fluctuations ranging from femtoseconds (e.g., bond vibrations) to seconds (e.g., ligand binding) and beyond, understanding their dynamics requires both experimental and theoretical investigation to bridge the timescale gap. Although molecular dynamics (MD) simulations have proven extremely powerful as a "computational microscope" for sampling the equilibrium distributions of biomolecules and probing their atomistic dynamics, many important biomolecular processes such as protein folding, ligand binding, or protein-protein association occur on timescales beyond the reach of direct simulation. MD simulation of slow biological processes such as these would require ∼1012 integration timesteps or more to observe a single millisecond-scale transition event, remaining intractable even for the fastest supercomputers today. In addition, understanding the mechanism of complex conformational changes requires obtaining statistics of these rare transitions between metastable states, compounding the computational requirements. The framework of transition path theory provides one approach to tackling this problem by considering statistics like the committor-which gives the probability of committing the product state before returning to a reactant state and is an ideal reaction coordinate-and the reactive flux, which maps the flow of reactive trajectories.Recently, researchers have developed an approach (known as the dynamical Galerkin approximation, or DGA) that leverages relatively short MD trajectories to compute these kinetic statistics by solving an dynamical operator equation projected onto a basis. The requisite elements in the resulting linear system are estimated via Monte Carlo averages over trajectory data. However, DGA suffers from challenges in constructing appropriately expressive basis sets and in data efficiency. Moreover, for most biological problems of interest, it remains challenging to extract mechanistic insight from the computed kinetic statistics, requiring human intuition to suggest physically informative coordinates that can appropriately describe the reaction dynamics. In this work, I consider two representative biological problems that illustrate the challenges of understanding mechanisms of slow conformational changes through MD simulations: how membrane proteins sense voltage and how metamorphic proteins switch structures. I also propose two ways of remedying the problems associated with DGA: first, by incorporating extra terms in the basis expansion to alleviate non-Markovian effects, and second, by representing the statistics using a neural network and learning them via an inexact iterative scheme based in numerical linear algebra. Together, the methods and applications in this work comprise a unified framework for understanding long-timescale biomolecular dynamics.
- 일반주제명
- Chemistry
- 일반주제명
- Biophysics
- 일반주제명
- Applied mathematics
- 일반주제명
- Computational chemistry
- 키워드
- Protein dynamics
- 키워드
- Voltage sensing
- 기본자료저록
- Dissertations Abstracts International. 87-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798293818969
■035 ▼a(MiAaPQ)AAI32237447
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a540
■1001 ▼aGuo, Spencer C.▼0(orcid)0000-0001-7899-8382
■24510▼aLong-Timescale Kinetics and Mechanisms of Protein Conformational Change
■260 ▼a[Sl]▼bThe University of Chicago▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a276 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-03, Section: B.
■500 ▼aAdvisor: Dinner, Aaron R.;Roux, Benoit.
■5021 ▼aThesis (Ph.D.)--The University of Chicago, 2025.
■520 ▼aBecause biomolecules are complex, high-dimensional systems exhibiting fluctuations ranging from femtoseconds (e.g., bond vibrations) to seconds (e.g., ligand binding) and beyond, understanding their dynamics requires both experimental and theoretical investigation to bridge the timescale gap. Although molecular dynamics (MD) simulations have proven extremely powerful as a "computational microscope" for sampling the equilibrium distributions of biomolecules and probing their atomistic dynamics, many important biomolecular processes such as protein folding, ligand binding, or protein-protein association occur on timescales beyond the reach of direct simulation. MD simulation of slow biological processes such as these would require ∼1012 integration timesteps or more to observe a single millisecond-scale transition event, remaining intractable even for the fastest supercomputers today. In addition, understanding the mechanism of complex conformational changes requires obtaining statistics of these rare transitions between metastable states, compounding the computational requirements. The framework of transition path theory provides one approach to tackling this problem by considering statistics like the committor-which gives the probability of committing the product state before returning to a reactant state and is an ideal reaction coordinate-and the reactive flux, which maps the flow of reactive trajectories.Recently, researchers have developed an approach (known as the dynamical Galerkin approximation, or DGA) that leverages relatively short MD trajectories to compute these kinetic statistics by solving an dynamical operator equation projected onto a basis. The requisite elements in the resulting linear system are estimated via Monte Carlo averages over trajectory data. However, DGA suffers from challenges in constructing appropriately expressive basis sets and in data efficiency. Moreover, for most biological problems of interest, it remains challenging to extract mechanistic insight from the computed kinetic statistics, requiring human intuition to suggest physically informative coordinates that can appropriately describe the reaction dynamics. In this work, I consider two representative biological problems that illustrate the challenges of understanding mechanisms of slow conformational changes through MD simulations: how membrane proteins sense voltage and how metamorphic proteins switch structures. I also propose two ways of remedying the problems associated with DGA: first, by incorporating extra terms in the basis expansion to alleviate non-Markovian effects, and second, by representing the statistics using a neural network and learning them via an inexact iterative scheme based in numerical linear algebra. Together, the methods and applications in this work comprise a unified framework for understanding long-timescale biomolecular dynamics.
■590 ▼aSchool code: 0330.
■650 4▼aChemistry
■650 4▼aBiophysics
■650 4▼aApplied mathematics
■650 4▼aComputational chemistry
■653 ▼aConformational dynamics
■653 ▼aMolecular dynamics
■653 ▼aProtein dynamics
■653 ▼aTransition path theory
■653 ▼aVoltage sensing
■690 ▼a0485
■690 ▼a0786
■690 ▼a0219
■690 ▼a0364
■71020▼aThe University of Chicago.
■7730 ▼tDissertations Abstracts International▼g87-03B.
■790 ▼a0330
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359414▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
