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: 10348826
• 负责人: Natarajan Kannan
• 金额: $ 45.02万
• 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/10348826
• 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 这项提案的总体目标是生成和实验测试未充分研究的“暗”激酶模型 发展和功能，并开发一个数据分析框架，用于假设生成和测试 暗细胞质组我们的工作假设是，综合挖掘可用的序列，结构和功能， 来自不同生物体的整个激酶组的数据（包括研究充分的激酶和暗激酶）将提供重要的 用于定义与暗激酶功能相关的序列和结构特征的上下文。作为初步 为了验证我们的假设，我们整合了与蛋白激酶相关的各种形式的数据，并将其概念化 结构，功能和进化的形式蛋白激酶本体论（ProKinO），并成功地 展示了本体论框架在确定人类关键知识差距中的应用 kinome和发现与蛋白激酶调节相关的关键残基/基序。我们提出建设 这些成功的研究，以实现以下两个目标。AIM 1将开发一种新的比较 激酶组学框架，其中暗激酶中的自然和疾病变体将在 PTM和蛋白质-蛋白质相互作用的背景下，以研究连接序列的关系， 结构、功能和管理。功能专门化的模型将在选定的实验测试 使用生物化学和基于细胞的测定， 机器可读格式，遵守可查找、可解释、可互操作和可重用（FAIR）数据规则。 AIM 2将为语义关联的蛋白激酶数据的复杂聚合查询构建一个独特的框架 从不同的来源和格式使用图形，易于使用的界面。研究人员将与 基于熟悉和直观的数据视图构建和制定查询。基于知识地图的 将开发交互式查询界面，研究人员可以使用该界面与ProKinO进行交互， 他们使用和理解的语义。ProKinO将与药物靶点本体（Pharos）正式建立联系 (DTO)和蛋白质本体论（PRO），以扩大社区外展和用户群。 拟议的研究预计将为知识发现提供统一的数据分析框架， 在暗激酶组上产生假设，并增强照亮可药用基因组的能力 (IDG)该联盟对暗激酶的生理作用进行了准确的预测。拟议 ProKinO与DTO和PRO的集成将增强这些本体在药物发现中的应用 并提供开源软件，用于为其他IDG目标（如离子）构建数据实例化本体， 通道和GPCR。预计这些成果反过来将加速对 可药物化的“暗”蛋白质组，并解决IDG将基因组数据转化为知识的倡议， 药物发现

项目成果

Nucleotide Binding, Evolutionary Insights, and Interaction Partners of the Pseudokinase Unc-51-like Kinase 4.

假激酶 Unc-51 样激酶 4 的核苷酸结合、进化见解和相互作用伙伴。

• DOI: 10.1016/j.str.2020.07.016
• 发表时间: 2020
• 期刊: Structure (London, England : 1993)
• 作者: Preuss,Franziska;Chatterjee,Deep;Mathea,Sebastian;Shrestha,Safal;St-Germain,Jonathan;Saha,Manipa;Kannan,Natarajan;Raught,Brian;Rottapel,Robert;Knapp,Stefan
• 通讯作者: Knapp,Stefan

Evolutionary and cellular analysis of the 'dark' pseudokinase PSKH2.

• DOI: 10.1042/bcj20220474
• 发表时间: 2023-01-31
• 期刊: The Biochemical journal