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Dynamic Membrane Interfaces Shape Biomolecular Structure and Function
Dynamic Membrane Interfaces Shape Biomolecular Structure and Function
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105659
- ISBN
- 9798263307639
- DDC
- 541
- 서명/저자
- Dynamic Membrane Interfaces Shape Biomolecular Structure and Function
- 발행사항
- [Sl] : University of Illinois at Urbana-Champaign, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 220 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Pogorelov, Taras.
- 학위논문주기
- Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2024.
- 초록/해제
- 요약The dynamic interplay of proteins, lipids, and small molecules within the cellular membrane is fundamental to critical biological processes. In this dissertation, I unravel the complexities of membrane dynamics through molecular simulations and bilayer modeling, addressing the nuanced interplay between lipids, proteins, and small molecules. By combining simulations, machine learning, and enhanced sampling techniques, I offer new insights into the mechanisms of protein-lipid interactions, the formation of amyloid fibrils, antimicrobial peptide bilayer disruption, and small-molecule modulators within cellular membranes. Chapter 1 motivates this thesis by discussing the diverse roles of lipids in biological membranes and their implications for cellular functionality. I discuss how lipid composition, including the presence of cholesterol and variations in phospholipid types, impacts the physical properties of membranes and their interactions with proteins. Chapter 2 focuses on the interactions of proteins and acidic lipids in the membrane, highlighting lactadherin's binding to phosphatidylserine and the broader implications for blood coagulation. This section examines the potential for targeted therapies by modulating the membrane binding mechanism. Continuing in Chapter 3, I investigate the membrane interactions with medin, dissecting its role in aortic amyloid fibril formation and contributing to understanding cardiovascular diseases. In Chapter 4, the discussion pivots to the disruption of bacterial membranes by antimicrobial peptides (AMPs), with a comprehensive analysis correlating AMP structures and properties to membrane disruption capabilities. Next, Chapter 5 discusses an innovative approach to mitigating climate change using bromoform from red seaweed as a ruminant food additive. Through membrane simulations and unsupervised machine learning, one can develop cellular engineering strategies to increase its storage in microalgae that can reduce methane emissions. Chapter 6 employs Markov State Models to characterize the kinetics and conformational landscape of the SWEET glucose transporter. This chapter also sheds light on the evolutionary connection between these transmembrane proteins and their bacterial homologs, SemiSWEET, offering molecular insight into differences in their transport process. Lastly, Chapter 7 tackles the challenges of simulating membrane proteins over extended timescales, introducing an adaptive sampling method motivated by unsupervised machine learning principles.
- 일반주제명
- Physical chemistry
- 일반주제명
- Biochemistry
- 일반주제명
- Biophysics
- 키워드
- Membranes
- 키워드
- Simulations
- 키워드
- Protein binding
- 기타저자
- University of Illinois at Urbana-Champaign School of Molecular & Cell Bio
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798263307639
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a541
■1001 ▼aCheng, Kevin Jose.
■24510▼aDynamic Membrane Interfaces Shape Biomolecular Structure and Function
■260 ▼a[Sl]▼bUniversity of Illinois at Urbana-Champaign▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a220 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Pogorelov, Taras.
■5021 ▼aThesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2024.
■520 ▼aThe dynamic interplay of proteins, lipids, and small molecules within the cellular membrane is fundamental to critical biological processes. In this dissertation, I unravel the complexities of membrane dynamics through molecular simulations and bilayer modeling, addressing the nuanced interplay between lipids, proteins, and small molecules. By combining simulations, machine learning, and enhanced sampling techniques, I offer new insights into the mechanisms of protein-lipid interactions, the formation of amyloid fibrils, antimicrobial peptide bilayer disruption, and small-molecule modulators within cellular membranes. Chapter 1 motivates this thesis by discussing the diverse roles of lipids in biological membranes and their implications for cellular functionality. I discuss how lipid composition, including the presence of cholesterol and variations in phospholipid types, impacts the physical properties of membranes and their interactions with proteins. Chapter 2 focuses on the interactions of proteins and acidic lipids in the membrane, highlighting lactadherin's binding to phosphatidylserine and the broader implications for blood coagulation. This section examines the potential for targeted therapies by modulating the membrane binding mechanism. Continuing in Chapter 3, I investigate the membrane interactions with medin, dissecting its role in aortic amyloid fibril formation and contributing to understanding cardiovascular diseases. In Chapter 4, the discussion pivots to the disruption of bacterial membranes by antimicrobial peptides (AMPs), with a comprehensive analysis correlating AMP structures and properties to membrane disruption capabilities. Next, Chapter 5 discusses an innovative approach to mitigating climate change using bromoform from red seaweed as a ruminant food additive. Through membrane simulations and unsupervised machine learning, one can develop cellular engineering strategies to increase its storage in microalgae that can reduce methane emissions. Chapter 6 employs Markov State Models to characterize the kinetics and conformational landscape of the SWEET glucose transporter. This chapter also sheds light on the evolutionary connection between these transmembrane proteins and their bacterial homologs, SemiSWEET, offering molecular insight into differences in their transport process. Lastly, Chapter 7 tackles the challenges of simulating membrane proteins over extended timescales, introducing an adaptive sampling method motivated by unsupervised machine learning principles.
■590 ▼aSchool code: 0090.
■650 4▼aPhysical chemistry
■650 4▼aBiochemistry
■650 4▼aBiophysics
■653 ▼aMolecular dynamics
■653 ▼aMembranes
■653 ▼aSimulations
■653 ▼aProtein binding
■690 ▼a0786
■690 ▼a0494
■690 ▼a0487
■71020▼aUniversity of Illinois at Urbana-Champaign▼bSchool of Molecular & Cell Bio.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0090
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17361055▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
