Computational design of novel protein binders based on structure mining and learning from data
基于结构挖掘和数据学习的新型蛋白质结合剂的计算设计
基本信息
- 批准号:10541909
- 负责人:
- 金额:$ 35.46万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-02-01 至 2024-12-31
- 项目状态:已结题
- 来源:
- 关键词:AccelerationAddressAffinityAgeAlgorithmsAmino Acid SequenceAntibodiesBackBasic ScienceBindingBinding ProteinsBinding SitesBiological AssayBiomedical ResearchCase StudyClinical ResearchCollectionCommunitiesComplementComplexComputer ModelsComputing MethodologiesCustomDataDevelopmentDiseaseDisease PathwayEngineeringFailureFeedbackGenerationsGeometryImageLabelLearningLibrariesLifeLigandsMethodologyMethodsMiningModernizationNatureOutcomePathway interactionsPatternPeptidesPharmaceutical PreparationsPhysiciansPropertyProtein Binding DomainProtein EngineeringProtein FamilyProteinsProtocols documentationPublishingReagentResearchRouteScientistSequence HomologsSignal TransductionSiteSourceSpecific qualifier valueSpecificityStructural ModelsStructureTechniquesTechnologyTertiary Protein StructureTestingTimeUpdateWorkdata librarydesigndisease diagnosisempowermentexperimental studyextracellularflexibilityhigh throughput screeningimprovedinhibitoriterative designlensmodel developmentnovelnovel strategiesprotein data bankprotein structurerepairedresearch and developmentscaffoldscreeningsmall moleculestatisticsstructural biologysuccesssynergismtechnology platformtherapeutic developmenttool
项目摘要
Our long-term objective is to turn computational protein design into a disruptive technology platform that will
enable the routine and rapid generation of reagents for detecting proteins or perturbing their functions. Currently,
research and therapy rely on small molecules and/or antibodies for these tasks. These are powerful tools, but
they can be slow and expensive to develop, and they do not meet all needs. Designer peptides or mini-proteins
have high potential to bind extracellular or intracellular targets either as labels (e.g., for imaging) or as functional
modulators (e.g., interaction inhibitors), for applications in basic and clinical research and in disease diagnosis
and treatment. Existing tools for designing such custom proteins rely on experimental library screening,
sometimes guided or supported by computational modeling of structure. Despite the immense value such
molecules would bring to basic biomedical research and therapeutic development, there are not yet rapid and
facile routes to obtaining designed proteins with desired properties. Computational methods can potentially
address this need, but existing technology is not sufficiently reliable, flexible or automated for routine use.
Compared to the mid-1990’s, when the modern approach to computational protein design was developed, we
live in a data and technology-empowered age. The premise of this proposal is that we can increase the range of
problems that can be solved using computational design, and also dramatically improve success rates, by
making full use of the proven rules of sequence-structure compatibility encoded in known natural structures and
their homologous sequences. The Protein Data Bank (the collection of all known protein structures) has grown
10-fold since 2000, placing us at a point where we can design novel proteins by constructing them from building
blocks used in nature. We have implemented a new design framework that is based on this principle and that is
different in fundamental aspects from all previously published alternatives. Tests on diverse tasks demonstrate
outstanding success. To further develop our approach, we propose methodological advances that we will
implement, test and then apply to protein design challenges involving detecting or inhibiting protein recognition
domains. We will develop and apply methods to: automatically identify design strategies for binding to a target
protein, score and rank specific design candidates, design libraries that will be screened to provide rich
experimental data about successes and failures, and automatically feed experimental data back into model
development in a principled way. Outcomes will include new methodology that will be shared with the community,
computational predictions of high-ranked interface design sites that can inform analysis of structures and
pathways, and experimentally validated designer molecules that bind to protein domains important for signaling
in disease pathways.
我们的长期目标是把计算蛋白设计变成一个颠覆性的技术平台
项目成果
期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs.
- DOI:10.1002/pro.4554
- 发表时间:2023-03
- 期刊:
- 影响因子:8
- 作者:Li, Alex J.;Lu, Mindren;Desta, Israel;Sundar, Vikram;Grigoryan, Gevorg;Keating, Amy E.
- 通讯作者:Keating, Amy E.
Tertiary motifs as building blocks for the design of protein-binding peptides.
- DOI:10.1002/pro.4322
- 发表时间:2022-06
- 期刊:
- 影响因子:8
- 作者:Swanson, Sebastian;Sivaraman, Venkatesh;Grigoryan, Gevorg;Keating, Amy E.
- 通讯作者:Keating, Amy E.
Multiplex measurement of protein-peptide dissociation constants using dialysis and mass spectrometry.
- DOI:10.1002/pro.4607
- 发表时间:2023-04
- 期刊:
- 影响因子:0
- 作者:
- 通讯作者:
Structure-conditioned amino-acid couplings: How contact geometry affects pairwise sequence preferences.
- DOI:10.1002/pro.4280
- 发表时间:2022-04
- 期刊:
- 影响因子:0
- 作者:Holland J;Grigoryan G
- 通讯作者:Grigoryan G
Data-driven computational protein design.
- DOI:10.1016/j.sbi.2021.03.009
- 发表时间:2021-08
- 期刊:
- 影响因子:6.8
- 作者:Frappier V;Keating AE
- 通讯作者:Keating AE
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Gevorg Grigoryan其他文献
Gevorg Grigoryan的其他文献
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{{ truncateString('Gevorg Grigoryan', 18)}}的其他基金
Computational design of novel protein binders based on structure mining and learning from data
基于结构挖掘和数据学习的新型蛋白质结合剂的计算设计
- 批准号:
10326369 - 财政年份:2020
- 资助金额:
$ 35.46万 - 项目类别:
Computational design of novel protein binders based on structure mining and learning from data
基于结构挖掘和数据学习的新型蛋白质结合剂的计算设计
- 批准号:
9887271 - 财政年份:2020
- 资助金额:
$ 35.46万 - 项目类别:
Computational design of novel protein binders based on structure mining and learning from data
基于结构挖掘和数据学习的新型蛋白质结合剂的计算设计
- 批准号:
10079500 - 财政年份:2020
- 资助金额:
$ 35.46万 - 项目类别:
Understanding transmembrane helix interaction on the structural level
在结构水平上理解跨膜螺旋相互作用
- 批准号:
7886793 - 财政年份:2009
- 资助金额:
$ 35.46万 - 项目类别:
Understanding transmembrane helix interaction on the structural level
在结构水平上理解跨膜螺旋相互作用
- 批准号:
7677038 - 财政年份:2009
- 资助金额:
$ 35.46万 - 项目类别:
Project 3: Protein Design for Selective Interference with LPA Signaling in Colon Cancer
项目 3:选择性干扰结肠癌 LPA 信号传导的蛋白质设计
- 批准号:
8813298 - 财政年份:
- 资助金额:
$ 35.46万 - 项目类别:
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