서브메뉴
검색
Deep Learning Guided Design of Dynamic Proteins
Deep Learning Guided Design of Dynamic Proteins
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152838
- ISBN
- 9798346875048
- DDC
- 610
- 저자명
- Guo, Amy.
- 서명/저자
- Deep Learning Guided Design of Dynamic Proteins
- 발행사항
- [Sl] : University of California, San Francisco, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 107 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
- 주기사항
- Advisor: Kortemme, Tanja.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Francisco, 2024.
- 초록/해제
- 요약Deep learning has greatly advanced design of highly stable static protein structures, but the controlled conformational dynamics that are hallmarks of natural switch-like signaling proteins have remained inaccessible to de novo design. In this dissertation, I review the fundamental principles and current advances in designing said conformational motions (Chapter 1) and then describe a general deep learning-guided approach for the de novo design of dynamic changes between intra-domain geometries of proteins, similar to switch mechanisms prevalent in nature, with atom-level precision (Chapter 2). In our study, we solved 4 structures validating the designed conformations, showed microsecond transitions between them, and demonstrated that the conformational landscape can be modulated by orthosteric ligands and allosteric mutations. Physics-based simulations were in remarkable agreement with deep learning predictions and experimental data, revealed distinct state-dependent residue interaction networks, and predicted mutations that tuned the designed conformational landscape. Our approach demonstrates that new modes of motion can now be realized through de novo design and provides a framework for constructing biology-inspired, tunable and controllable protein signaling behavior de novo. Finally, in Chapter 3, I discuss key areas where further multi-state tool development is needed and promising applications for de novo dynamics design in the near future.
- 일반주제명
- Bioengineering
- 일반주제명
- Biomedical engineering
- 일반주제명
- Biochemistry
- 키워드
- Protein design
- 키워드
- Protein dynamics
- 키워드
- Deep learning
- 기타저자
- University of California, San Francisco Bioengineering
- 기본자료저록
- Dissertations Abstracts International. 86-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008250123s2024 us c eng d■001000017164156
■00520250211152838
■006m o d
■007cr#unu||||||||
■020 ▼a9798346875048
■035 ▼a(MiAaPQ)AAI31562010
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a610
■1001 ▼aGuo, Amy.▼0(orcid)0000-0003-4775-9498
■24510▼aDeep Learning Guided Design of Dynamic Proteins
■260 ▼a[Sl]▼bUniversity of California, San Francisco▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a107 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-06, Section: B.
■500 ▼aAdvisor: Kortemme, Tanja.
■5021 ▼aThesis (Ph.D.)--University of California, San Francisco, 2024.
■520 ▼aDeep learning has greatly advanced design of highly stable static protein structures, but the controlled conformational dynamics that are hallmarks of natural switch-like signaling proteins have remained inaccessible to de novo design. In this dissertation, I review the fundamental principles and current advances in designing said conformational motions (Chapter 1) and then describe a general deep learning-guided approach for the de novo design of dynamic changes between intra-domain geometries of proteins, similar to switch mechanisms prevalent in nature, with atom-level precision (Chapter 2). In our study, we solved 4 structures validating the designed conformations, showed microsecond transitions between them, and demonstrated that the conformational landscape can be modulated by orthosteric ligands and allosteric mutations. Physics-based simulations were in remarkable agreement with deep learning predictions and experimental data, revealed distinct state-dependent residue interaction networks, and predicted mutations that tuned the designed conformational landscape. Our approach demonstrates that new modes of motion can now be realized through de novo design and provides a framework for constructing biology-inspired, tunable and controllable protein signaling behavior de novo. Finally, in Chapter 3, I discuss key areas where further multi-state tool development is needed and promising applications for de novo dynamics design in the near future.
■590 ▼aSchool code: 0034.
■650 4▼aBioengineering
■650 4▼aBiomedical engineering
■650 4▼aBiochemistry
■653 ▼aProtein design
■653 ▼aProtein dynamics
■653 ▼aDeep learning
■653 ▼aProtein signaling
■690 ▼a0202
■690 ▼a0541
■690 ▼a0487
■71020▼aUniversity of California, San Francisco▼bBioengineering.
■7730 ▼tDissertations Abstracts International▼g86-06B.
■790 ▼a0034
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164156▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
