자료유형  

 학위논문

Control Number  

0017164156

International Standard Book Number  

9798346875048

Dewey Decimal Classification Number  

610

Main Entry-Personal Name  

Guo, Amy.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, San Francisco., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

107 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

General Note  

Advisor: Kortemme, Tanja.

Dissertation Note  

Thesis (Ph.D.)--University of California, San Francisco, 2024.

Summary, Etc.  

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.

Subject Added Entry-Topical Term  

Bioengineering.

Subject Added Entry-Topical Term  

Biomedical engineering.

Subject Added Entry-Topical Term  

Biochemistry.

Index Term-Uncontrolled  

Protein design

Index Term-Uncontrolled  

Protein dynamics

Index Term-Uncontrolled  

Deep learning

Index Term-Uncontrolled  

Protein signaling

Added Entry-Corporate Name  

University of California, San Francisco Bioengineering

Host Item Entry  

Dissertations Abstracts International. 86-06B.

Electronic Location and Access  

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Control Number  

joongbu:658604