A data analytics framework for mining the dark kinome

用于挖掘暗激酶组的数据分析框架

• 批准号: 9915864
• 负责人: Natarajan Kannan
• 金额: $ 43.85万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9915864
• 关键词: Address; Allosteric Site; Biochemical; Bioinformatics; Biological Assay; Calcium; Calmodulin; Cells; Community Outreach; Complex; Computer software; Data; Data Analytics; Disease; Drug Targeting; Evolution; FAIR principles; Foundations; G-Protein-Coupled Receptors; Generations; Genome; Genomics; Goals; Human; Human Genome; Intuition; Ion Channel; Knowledge; Knowledge Discovery; Link; Malignant Neoplasms; Maps; Mining; Mission; Modeling; Mutation; Ontology; Organism; Outcome; Pathway interactions; Pattern; Pharmacogenomics; Phosphotransferases; Physiological; Positioning Attribute; Post-Translational Protein Processing; Property; Protein Analysis; Protein Family; Protein Kinase; Protein-Serine-Threonine Kinases; Proteins; Proteome; Readability; Regulation; Research Personnel; Resources; Role; Semantics; Sequence Alignment; Signal Transduction; Source; Structure; Testing; Therapeutic; Translating; Variant; Visualization; Visualization software; base; comparative; computer based Semantic Analysis; data integration; data mining; data reuse; data to knowledge; data tools; drug discovery; drug sensitivity; genome resource; genomic data; human disease; informatics tool; interest; link protein; novel; open source; personalized medicine; protein protein interaction; tool; user-friendly

Project Summary The overall goal of this proposal is to generate and experimentally test models of understudied “dark” kinase evolution and function, and to develop a data-analytics framework for hypothesis generation and testing on the dark kinome. Our working hypothesis is that integrative mining of available sequence, structure, and functional data on the entire kinome from diverse organisms (both well-studied and dark kinases) will provide important context for defining sequence and structural features associated with dark kinase functions. As a preliminary test of our hypothesis, we have integrated and conceptualized diverse forms of data related to protein kinase structure, function, and evolution in the form of the Protein Kinase Ontology (ProKinO), and successfully demonstrated the application of an ontological framework in identifying key knowledge gaps in the human kinome and in discovering key residues/motifs associated with protein kinase regulation. We propose to build on these successful studies to accomplish the following two aims. Aim1 will develop a novel comparative kinomics framework in which natural and disease variants in dark kinases will be correlated and visualized in the context of PTMs and protein-protein interactions to investigate the relationships connecting sequence, structure, function and regulation. Models of functional specialization will be experimentally tested in selected dark and pseudokinases using biochemical and cell-based assays and made publically available in human and machine-readable format, adhering to Findable, Accessible, Interoperable and Reusable (FAIR) data rules. Aim2 will build a unique framework for complex aggregate queries on semantically linked protein kinase data from disparate sources and formats using graphical, easy to use interfaces. Researchers will interact with the framework and formulate queries based on a familiar and intuitive view of the data. A knowledge map-based interactive query interface will be developed using which researchers can interact with ProKinO using semantics they use and understand. ProKinO will be formally linked with Pharos, the Drug Target Ontology (DTO) and the Protein Ontology (PRO) to expand community outreach and user base. The proposed studies are expected to provide a unified data analytics framework for knowledge discovery and hypothesis generation on the dark kinome and enhance the ability of the Illuminating the Druggable Genome (IDG) consortium to make accurate predictions about the physiological roles of dark kinases. The proposed integration of ProKinO with DTO and PRO will enhance the application of these ontologies in drug discovery and provide open source software for building data instantiated ontologies for other IDG targets such as ion- channels and GPCRs. These outcomes, in turn, are expected to accelerate the functional characterization of the druggable “dark” proteome and address the IDG initiative of translating genomic data into knowledge for drug discovery.

项目摘要 该提案的总体目标是生成和实验测试的“黑暗”激酶的模型 进化和功能，并为假设生成和测试开发数据分析框架 黑暗的动物。我们的工作假设是可用序列，结构和功能的整合挖掘 来自不同生物体的整个Kinome的数据（研究良好和深色激酶）将提供重要的 定义与深色激酶功能相关的序列和结构特征的上下文。作为初步 检验我们的假设，我们整合了与蛋白激酶有关的潜水员形式的研究形式 蛋白激酶本体论（Prokino）形式的结构，功能和进化，并成功地 证明了本体论框架在识别人类关键知识差距中的应用 在发现与蛋白激酶调节相关的关键残差/基序时。我们建议建造 在这些成功的研究中，以实现以下两个目标。 AIM1将发展一个新颖的比较 激酶中的天然和疾病变体将在黑暗激酶中进行相关和可视化的框架，其中 PTMS和蛋白质 - 蛋白质相互作用的上下文，以研究连接序列的关系， 结构，功能和调节。功能专业的模型将在选定中进行实验测试 使用生化和基于细胞的测定的黑暗和假子酶，并在人类和 机器可读格式，遵守可找到的，可访问的，可互操作的和可重复使用的（公平）数据规则。 AIM2将在语义链接的蛋白激酶数据上建立一个独特的框架，以用于复杂的骨料查询 使用图形，易于使用的接口的不同来源和格式。研究人员将与 框架和基于数据的熟悉和直观视图制定查询。基于知识图的 将使用研究人员可以使用这些互动查询界面来开发该界面 他们使用和理解的语义。 Prokino将与Pharas正式相关，Pharas是药物的本体论 （DTO）和蛋白质本体论（PRO），以扩大社区外展和用户群。 预计拟议的研究将为知识发现提供统一的数据分析框架 在黑暗的基因组上产生假设，并增强了照明可毒基因组的能力 （IDG）财团对深色激酶的物理作用进行准确的预测。提议 Prokino与DTO和PRO的整合将增强这些本体在药物发现中的应用 并提供用于构建数据实例化的数据的开源软件，用于其他IDG目标，例如离子 - 频道和GPCR。这些结果又有望加速 可吸毒的“黑暗”蛋白质组，并针对IDG倡议，将基因组数据转化为知识的知识 药物发现。