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Deep Learning Tools for Protein Binder Design- [electronic resource]
![Deep Learning Tools for Protein Binder Design - [electronic resource]](/Users/Baul/Images/book.png)
Deep Learning Tools for Protein Binder Design- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101207
- ISBN
- 9798379905804
- DDC
- 574
- 서명/저자
- Deep Learning Tools for Protein Binder Design - [electronic resource]
- 발행사항
- [S.l.]: : University of Washington., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(95 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
- 주기사항
- Advisor: Baker, David.
- 학위논문주기
- Thesis (Ph.D.)--University of Washington, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약The ability to design protein-binding proteins is broadly useful. In this dissertation I will show our work to develop a deep learning-based pipeline for protein binder design. I will show how we configured AlphaFold2 to classify in silico designs which are likely to bind from those which are not likely to bind. I will then demonstrate how we can use the ProteinMPNN model, in combination with classical Rosetta protocols, to perform efficient sequence design on binder backbones. Finally, I will show how we trained a denoising diffusion model to generate protein backbones and how this can be used to massively accelerate the binder design pipeline. This deep learning-based pipeline is faster, easier to use, and has much higher experimental success rates than the previous Rosetta-based pipeline.
- 일반주제명
- Biochemistry.
- 일반주제명
- Biomedical engineering.
- 키워드
- Binder design
- 키워드
- Deep learning
- 기타저자
- University of Washington Molecular Engineering and Sciences
- 기본자료저록
- Dissertations Abstracts International. 85-01B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101207
■006m o d
■007cr#unu||||||||
■020 ▼a9798379905804
■035 ▼a(MiAaPQ)AAI30525189
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aBennett, Nathaniel Richard.
■24510▼aDeep Learning Tools for Protein Binder Design▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of Washington. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(95 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-01, Section: B.
■500 ▼aAdvisor: Baker, David.
■5021 ▼aThesis (Ph.D.)--University of Washington, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aThe ability to design protein-binding proteins is broadly useful. In this dissertation I will show our work to develop a deep learning-based pipeline for protein binder design. I will show how we configured AlphaFold2 to classify in silico designs which are likely to bind from those which are not likely to bind. I will then demonstrate how we can use the ProteinMPNN model, in combination with classical Rosetta protocols, to perform efficient sequence design on binder backbones. Finally, I will show how we trained a denoising diffusion model to generate protein backbones and how this can be used to massively accelerate the binder design pipeline. This deep learning-based pipeline is faster, easier to use, and has much higher experimental success rates than the previous Rosetta-based pipeline.
■590 ▼aSchool code: 0250.
■650 4▼aBiochemistry.
■650 4▼aBiomedical engineering.
■653 ▼aBinder design
■653 ▼aRosetta protocols
■653 ▼aDeep learning
■653 ▼aProtein backbones
■653 ▼aDenoising diffusion
■690 ▼a0487
■690 ▼a0541
■71020▼aUniversity of Washington▼bMolecular Engineering and Sciences.
■7730 ▼tDissertations Abstracts International▼g85-01B.
■773 ▼tDissertation Abstract International
■790 ▼a0250
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933130▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
