Genome-wide structure-based analysis of protein-protein interactions and networks
基于全基因组结构的蛋白质-蛋白质相互作用和网络分析
基本信息
- 批准号:10542796
- 负责人:
- 金额:$ 40.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-01-01 至 2025-12-31
- 项目状态:未结题
- 来源:
- 关键词:AddressAlgorithmsAreaBacterial InfectionsBiologicalBiologyBiomedical ResearchCell LineCell physiologyCellsChemicalsDatabasesDevelopmentDiseaseGenerationsGenesGeneticGenomeGoalsHumanIndividualLinkMalignant NeoplasmsMapsMethodologyMethodsModelingMolecularNucleic AcidsPatientsPharmaceutical PreparationsPharmacotherapyProtein AnalysisProteinsProteomeResearchResourcesSoftware ToolsStructureSystems BiologyTissuesVirusVirus DiseasesVisionWorkdruggable targetgenome-widegenomic datahuman diseaseinsightlink proteinmachine learning methodnovelpathogenic bacteriaprotein functionprotein protein interactionprotein structureprotein structure predictionsmall moleculesoftware developmentstructural biologythree dimensional structuretooltumor
项目摘要
Project Summary
Our lab works in the general area of computational structural biology although it also includes an
experimental component. We carry out theoretical and computational research and develop software tools, with
our efforts being guided by a variety of applications of biomedical importance. In the past we have elucidated
the structural and energetic origins of protein-protein and protein-nucleic acid interactions, developed methods
for protein structure prediction, and detected novel structural and functional relationships between proteins based
on their geometric similarity. We currently focus on two distinct areas: the exploitation of structural information
to predict protein function on a genome-wide scale and the molecular basis of cell-cell recognition. The former
topic is the subject of the current proposal which focuses on the prediction of protein-protein interactions (PPIs)
and protein interaction networks. Our overarching goal is to provide a structure-informed perspective in multiple
areas of systems biology, thus filling a major gap in this rapidly growing area of biomedical research.
Our research plans are derived from our development of the PrePPI algorithm and corresponding
database of human PPIs. PrePPI provides proteome-wide structure-based predictions of PPIs, and discovers
relationships not obtainable from other methods. The P-HIPSTer algorithm, which is derived from PrePPI, offers
analogous information for virus-human PPIs for 1000 human-infecting viruses. The reliability of both resources
has been validated experimentally, and both have revealed novel biological insights. PrePPI, in common with
other PPI databases, is cell-context independent and, for example, does not distinguish among tissue and tumor
types. To address this challenge, we developed the OncoSig algorithm that uses machine learning methods to
combine PrePPI with regulatory interactions from patient genomic data. The generation of tumor-specific lists of
PPIs, called SigSets, can then be mapped onto a context-dependent PPI network, or SigMap. We have also
developed novel methodologies that link protein structure space with chemical compound space.
The current proposal builds on these accomplishments with new methodological developments and new
applications to network biology. We plan to integrate PrePPI with PPI information derived from genetic
interactions derived from the correlation of gene profiles across many conditions (e.g. tumor types, cell lines or
drug treatments). This will provide an unprecedented structure- and context-dependent view of protein interaction
networks. Other plans include the extension of PrePPI to non-human genomes and the extension of P-HIPSTer
to bacterial pathogens. Our overall vision includes the development of an integrated set of software tools and
databases that will advance cutting edge biomedical applications. These tools will range in scope from protein-
protein interaction networks, structure-derived protein function annotation and to the linking of network biology
to chemical compound space which will suggest druggable targets within networks and provide leads for small
molecules that can target individual proteins.
项目总结
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
BARRY H HONIG其他文献
BARRY H HONIG的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('BARRY H HONIG', 18)}}的其他基金
Structure-informed dissection of cancer-specific intracellular and paracrine networks
癌症特异性细胞内和旁分泌网络的结构知情解剖
- 批准号:
10729385 - 财政年份:2023
- 资助金额:
$ 40.5万 - 项目类别:
Genome-wide structure-based analysis of protein-protein interactions and networks
基于全基因组结构的蛋白质-蛋白质相互作用和网络分析
- 批准号:
10320837 - 财政年份:2021
- 资助金额:
$ 40.5万 - 项目类别:
Genome-wide structure-based analysis of protein-protein interactions and networks
基于全基因组结构的蛋白质-蛋白质相互作用和网络分析
- 批准号:
10809330 - 财政年份:2021
- 资助金额:
$ 40.5万 - 项目类别:
相似海外基金
Approximate algorithms and architectures for area efficient system design
区域高效系统设计的近似算法和架构
- 批准号:
LP170100311 - 财政年份:2018
- 资助金额:
$ 40.5万 - 项目类别:
Linkage Projects
AMPS: Rank Minimization Algorithms for Wide-Area Phasor Measurement Data Processing
AMPS:用于广域相量测量数据处理的秩最小化算法
- 批准号:
1736326 - 财政年份:2017
- 资助金额:
$ 40.5万 - 项目类别:
Standard Grant
Low Power, Area Efficient, High Speed Algorithms and Architectures for Computer Arithmetic, Pattern Recognition and Cryptosystems
用于计算机算术、模式识别和密码系统的低功耗、面积高效、高速算法和架构
- 批准号:
1686-2013 - 财政年份:2017
- 资助金额:
$ 40.5万 - 项目类别:
Discovery Grants Program - Individual
Rigorous simulation of speckle fields caused by large area rough surfaces using fast algorithms based on higher order boundary element methods
使用基于高阶边界元方法的快速算法对大面积粗糙表面引起的散斑场进行严格模拟
- 批准号:
375876714 - 财政年份:2017
- 资助金额:
$ 40.5万 - 项目类别:
Research Grants
Low Power, Area Efficient, High Speed Algorithms and Architectures for Computer Arithmetic, Pattern Recognition and Cryptosystems
用于计算机算术、模式识别和密码系统的低功耗、面积高效、高速算法和架构
- 批准号:
1686-2013 - 财政年份:2016
- 资助金额:
$ 40.5万 - 项目类别:
Discovery Grants Program - Individual
Low Power, Area Efficient, High Speed Algorithms and Architectures for Computer Arithmetic, Pattern Recognition and Cryptosystems
用于计算机算术、模式识别和密码系统的低功耗、面积高效、高速算法和架构
- 批准号:
1686-2013 - 财政年份:2015
- 资助金额:
$ 40.5万 - 项目类别:
Discovery Grants Program - Individual
Low Power, Area Efficient, High Speed Algorithms and Architectures for Computer Arithmetic, Pattern Recognition and Cryptosystems
用于计算机算术、模式识别和密码系统的低功耗、面积高效、高速算法和架构
- 批准号:
1686-2013 - 财政年份:2014
- 资助金额:
$ 40.5万 - 项目类别:
Discovery Grants Program - Individual
AREA: Optimizing gene expression with mRNA free energy modeling and algorithms
区域:利用 mRNA 自由能建模和算法优化基因表达
- 批准号:
8689532 - 财政年份:2014
- 资助金额:
$ 40.5万 - 项目类别:
CPS: Synergy: Collaborative Research: Distributed Asynchronous Algorithms and Software Systems for Wide-Area Monitoring of Power Systems
CPS:协同:协作研究:用于电力系统广域监控的分布式异步算法和软件系统
- 批准号:
1329780 - 财政年份:2013
- 资助金额:
$ 40.5万 - 项目类别:
Standard Grant
CPS: Synergy: Collaborative Research: Distributed Asynchronous Algorithms and Software Systems for Wide-Area Mentoring of Power Systems
CPS:协同:协作研究:用于电力系统广域指导的分布式异步算法和软件系统
- 批准号:
1329745 - 财政年份:2013
- 资助金额:
$ 40.5万 - 项目类别:
Standard Grant














{{item.name}}会员




