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Developing Computational and Experimental Methods to Study Context-Specific Global Protein-Protein Interaction Networks Applied to Map Protein-Protein Interaction Network Dynamics During Viral Infections
Developing Computational and Experimental Methods to Study Context-Specific Global Protein-Protein Interaction Networks Applied to Map Protein-Protein Interaction Network Dynamics During Viral Infections
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103520
- ISBN
- 9798280747050
- DDC
- 574
- 서명/저자
- Developing Computational and Experimental Methods to Study Context-Specific Global Protein-Protein Interaction Networks Applied to Map Protein-Protein Interaction Network Dynamics During Viral Infections
- 발행사항
- [Sl] : Princeton University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 239 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Cristea, Ileana M.;Troyanskaya, Olga G.
- 학위논문주기
- Thesis (Ph.D.)--Princeton University, 2025.
- 초록/해제
- 요약Protein-protein interactions (PPIs) orchestrate cellular functions and responses to environmental changes, making their dynamic characterization essential for understanding biological systems. Here, we advance the study of global and spatially resolved PPI networks through innovative experimental and computational proteomic approaches. First, we develop Tapioca, an ensemble machine-learning framework that integrates mass spectrometry interactome data with protein physical properties, domains and tissue-specific functional networks to predict global PPI networks in dynamic contexts. Furthermore, we optimize the thermal proximity coaggregation (TPCA) workflow, improving the usefulness of data produced from this method for downstream PPI prediction. Leveraging these experimental optimizations and Tapioca, we investigate temporal PPIs during Kaposi's sarcoma-associated herpesvirus (KSHV) reactivation, identifying NUCKS as a broad-spectrum herpesvirus proviral factor.Expanding on these methodologies, to enable the study of global PPI networks with subcellular resolution, we develop a nuclear/cytoplasmic TPCA workflow. Applying this new workflow to study the spatiotemporal PPI network dynamics during herpes simplex virus type I (HSV-1) infection we resolve compartment-specific PPI regulation. Additionally, we uncover new residents and functions for the HSV-1 infection formed structures virus-induced chaperone-enriched (VICE) domains. Specifically, we find that VICE domains sequester ribosome biogenesis factors and disrupt cellular translation. Finally, we systematically compare TPCA with ion-based proteome-integrated solubility alteration (I-PISA), uncovering how protein properties such as size, hydrophobicity, and localization influence PPI detection. Incorporating insoluble fractions into these workflows broadens the detectable PPI landscape and enhances network resolution. Moreover, we demonstrate that label-free data-independent acquisition (DIA) TPCA maintains robust PPI prediction quality despite minimal sample input, enabling its application in sample-limited experimental contexts.Together, these studies refine denaturation-based proteomic methods for the study of global PPI networks, highlight the impact of protein properties on PPI detection, and provide scalable strategies for mapping dynamic PPI networks in diverse biological contexts.
- 일반주제명
- Bioinformatics
- 일반주제명
- Virology
- 일반주제명
- Systematic biology
- 일반주제명
- Cellular biology
- 키워드
- Viral infection
- 기타저자
- Princeton University Quantitative Computational Biology
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798280747050
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aReed, Tavis Jahi.▼0(orcid)0000-0002-0945-5461
■24510▼aDeveloping Computational and Experimental Methods to Study Context-Specific Global Protein-Protein Interaction Networks Applied to Map Protein-Protein Interaction Network Dynamics During Viral Infections
■260 ▼a[Sl]▼bPrinceton University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a239 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Cristea, Ileana M.;Troyanskaya, Olga G.
■5021 ▼aThesis (Ph.D.)--Princeton University, 2025.
■520 ▼aProtein-protein interactions (PPIs) orchestrate cellular functions and responses to environmental changes, making their dynamic characterization essential for understanding biological systems. Here, we advance the study of global and spatially resolved PPI networks through innovative experimental and computational proteomic approaches. First, we develop Tapioca, an ensemble machine-learning framework that integrates mass spectrometry interactome data with protein physical properties, domains and tissue-specific functional networks to predict global PPI networks in dynamic contexts. Furthermore, we optimize the thermal proximity coaggregation (TPCA) workflow, improving the usefulness of data produced from this method for downstream PPI prediction. Leveraging these experimental optimizations and Tapioca, we investigate temporal PPIs during Kaposi's sarcoma-associated herpesvirus (KSHV) reactivation, identifying NUCKS as a broad-spectrum herpesvirus proviral factor.Expanding on these methodologies, to enable the study of global PPI networks with subcellular resolution, we develop a nuclear/cytoplasmic TPCA workflow. Applying this new workflow to study the spatiotemporal PPI network dynamics during herpes simplex virus type I (HSV-1) infection we resolve compartment-specific PPI regulation. Additionally, we uncover new residents and functions for the HSV-1 infection formed structures virus-induced chaperone-enriched (VICE) domains. Specifically, we find that VICE domains sequester ribosome biogenesis factors and disrupt cellular translation. Finally, we systematically compare TPCA with ion-based proteome-integrated solubility alteration (I-PISA), uncovering how protein properties such as size, hydrophobicity, and localization influence PPI detection. Incorporating insoluble fractions into these workflows broadens the detectable PPI landscape and enhances network resolution. Moreover, we demonstrate that label-free data-independent acquisition (DIA) TPCA maintains robust PPI prediction quality despite minimal sample input, enabling its application in sample-limited experimental contexts.Together, these studies refine denaturation-based proteomic methods for the study of global PPI networks, highlight the impact of protein properties on PPI detection, and provide scalable strategies for mapping dynamic PPI networks in diverse biological contexts.
■590 ▼aSchool code: 0181.
■650 4▼aBioinformatics
■650 4▼aVirology
■650 4▼aSystematic biology
■650 4▼aCellular biology
■653 ▼aProtein interaction dynamics
■653 ▼aProtein-protein interactions
■653 ▼aThermal proximity coaggregation
■653 ▼aViral infection
■690 ▼a0715
■690 ▼a0720
■690 ▼a0423
■690 ▼a0379
■71020▼aPrinceton University▼bQuantitative Computational Biology.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0181
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357493▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
