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Constraint-Based Methods for Macromolecular Structure Prediction and Multi-Conformer Refinement
Constraint-Based Methods for Macromolecular Structure Prediction and Multi-Conformer Refinement
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105157
- ISBN
- 9798273306967
- DDC
- 530
- 서명/저자
- Constraint-Based Methods for Macromolecular Structure Prediction and Multi-Conformer Refinement
- 발행사항
- [Sl] : Cornell University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 111 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-07, Section: B.
- 주기사항
- Advisor: Elser, Veit.
- 학위논문주기
- Thesis (Ph.D.)--Cornell University, 2025.
- 초록/해제
- 요약This dissertation addresses fundamental challenges in structural biology through the development and application of constraint satisfaction formulations, which are effectively solved using iterative projection methods such as Reflect-Reflect-Relax (RRR). We demonstrate how the divide-and-concur framework, when combined with these methods, can successfully tackle complex structural problems that require the satisfaction of multiple constraints involving molecular geometry, X-ray contrast (electron charge density), and energy models. We begin with a proof-of-concept study involving simplified two-dimensional lattice proteins, composed of only two residues, H (hydrophobic) and P (polar), designed to capture the essential features of globular protein folding. This study establishes the viability of constraint-based approaches for structural prediction problems, demonstrating their effectiveness in solving such systems. Building upon this foundation, we utilize the divide-and-concur framework to address the real protein folding challenge. While protein folding involves complex thermodynamic processes that require a delicate balance of multiple physical forces, our constraint-based approach effectively approximates traditional force field models while offering significant computational advantages. The key innovation lies in incorporating interpretable constraints through the divide-and-concur framework, which systematically reduces the conformational search space. This enhancement enables ab initio folding of miniproteins on standard commercial computers within minutes, representing a substantial improvement in computational efficiency. The versatility of constraint-based methods extends beyond protein folding to other critical structural biology applications, particularly in multi-conformer refinement problems where configuration tangling phenomena arise. The modular nature of the divide-and-concur framework facilitates seamless integration of density constraints, making it particularly well-suited for multi-constraint optimization problems. This work establishes constraint-based approaches as powerful tools for tackling some of the most computationally demanding problems in modern structural biology.
- 일반주제명
- Physics
- 일반주제명
- Biology
- 일반주제명
- Molecular biology
- 키워드
- Lattice proteins
- 기타저자
- Cornell University Physics
- 기본자료저록
- Dissertations Abstracts International. 87-07B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798273306967
■035 ▼a(MiAaPQ)AAI32243434
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a530
■1001 ▼aMandaiya, Avinash.
■24510▼aConstraint-Based Methods for Macromolecular Structure Prediction and Multi-Conformer Refinement
■260 ▼a[Sl]▼bCornell University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a111 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-07, Section: B.
■500 ▼aAdvisor: Elser, Veit.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2025.
■520 ▼aThis dissertation addresses fundamental challenges in structural biology through the development and application of constraint satisfaction formulations, which are effectively solved using iterative projection methods such as Reflect-Reflect-Relax (RRR). We demonstrate how the divide-and-concur framework, when combined with these methods, can successfully tackle complex structural problems that require the satisfaction of multiple constraints involving molecular geometry, X-ray contrast (electron charge density), and energy models. We begin with a proof-of-concept study involving simplified two-dimensional lattice proteins, composed of only two residues, H (hydrophobic) and P (polar), designed to capture the essential features of globular protein folding. This study establishes the viability of constraint-based approaches for structural prediction problems, demonstrating their effectiveness in solving such systems. Building upon this foundation, we utilize the divide-and-concur framework to address the real protein folding challenge. While protein folding involves complex thermodynamic processes that require a delicate balance of multiple physical forces, our constraint-based approach effectively approximates traditional force field models while offering significant computational advantages. The key innovation lies in incorporating interpretable constraints through the divide-and-concur framework, which systematically reduces the conformational search space. This enhancement enables ab initio folding of miniproteins on standard commercial computers within minutes, representing a substantial improvement in computational efficiency. The versatility of constraint-based methods extends beyond protein folding to other critical structural biology applications, particularly in multi-conformer refinement problems where configuration tangling phenomena arise. The modular nature of the divide-and-concur framework facilitates seamless integration of density constraints, making it particularly well-suited for multi-constraint optimization problems. This work establishes constraint-based approaches as powerful tools for tackling some of the most computationally demanding problems in modern structural biology.
■590 ▼aSchool code: 0058.
■650 4▼aPhysics
■650 4▼aBiology
■650 4▼aMolecular biology
■653 ▼aReflect-Reflect-Relax
■653 ▼aLattice proteins
■653 ▼aComputational efficiency
■653 ▼aStructural biology
■690 ▼a0605
■690 ▼a0306
■690 ▼a0307
■71020▼aCornell University▼bPhysics.
■7730 ▼tDissertations Abstracts International▼g87-07B.
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359679▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
