Unlocking sequence-structure-function-disease relationships in large protein super-families

解锁大型蛋白质超家族中的序列-结构-功能-疾病关系

• 批准号: 10552630
• 负责人: Natarajan Kannan
• 金额: $ 44.43万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10552630
• 关键词: Acceleration; Address; Allosteric Regulation; Allosteric Site; Biochemical; Biological Assay; Biology; Complex; Computer Models; Cysteine; Data Set; Diabetes Mellitus; Disease; Gene Family; Genome; Genotype; Goals; Inflammatory; Link; Machine Learning; Malignant Neoplasms; Maps; Mass Spectrum Analysis; Mining; Modeling; Mutation; Oncogenic; Oxidation-Reduction; Oxidative Stress; Patients; Phosphotransferases; Protein Kinase; Proteins; Receptor Protein-Tyrosine Kinases; Regulation; Resources; Signal Transduction; Site; Structure; System; Technology; Work; age related; biological adaptation to stress; data integration; disease phenotype; drug discovery; glycosyltransferase; human disease; in vivo; knowledge graph; member; molecular dynamics; personalized medicine; phenome; predictive modeling; protein function; small molecule; sulfotransferase; tool

Project Summary Predicting disease phenotypes from genotypes is a grand challenge in biology and personalized medicine. Our long-term goal is to address this challenge using a combination of computational and experimental approaches. Working towards this goal, we have developed and deployed a powerful evolutionary systems approach to map the complex relationships connecting sequence, structure, function, regulation and disease in biomedically important protein super-families such as protein kinases. We have made important contributions describing the unique modes of allosteric regulation in various protein kinases, deciphering the structural basis of oncogenic activation in a subset of receptor tyrosine kinases, uncovering the regulation of pseudokinases, and developing new tools and resources for addressing data integration challenges in the signaling field. We propose to build on these impactful studies to answer key questions emanating from our ongoing studies such as: What are the functions of pseudokinases, the catalytically-inert members of the kinome, and how can we use pseudokinases to better predict and characterize non-catalytic functions of kinases? What are the functions of conserved cysteine residues in regulatory sites of protein and small molecule kinases and are they post-translationally modified in redox signaling and oxidative stress response that are causally associated with age-related disorders? How can we enhance existing computational models for predicting genome-phenome relationships using structural information, and can machine learning on structurally enhanced knowledge graphs reveal new relationships between patient-derived mutations and disease phenotypes? We propose to answer these questions using a variety of approaches including statistical mining of large sequence datasets, molecular dynamics simulations, machine learning, mass spectrometry, biochemical analysis and in vivo assays. Completion of this work is expected to reveal new allosteric sites for targeting pseudokinase and kinase non-catalytic functions in diseases, and significantly advance our understanding of kinase regulatory mechanisms in disease and normal states. Our work will create new tools and resources for knowledge graph mining and provide explainable models for inferring causal relationships linking genomes and phenomes with potential applications in personalized medicine. Finally, the scope and impact of our work will be significantly broadened by participation in studies extending our specialized tools and technological approaches developed for the study of kinases to other biomedically important gene families such as glycosyltransferases and sulfotransferases.

项目摘要 从基因型预测疾病表型是生物学和个性化医疗的巨大挑战。我们 长期目标是通过计算和实验相结合来解决这一挑战 接近。为了实现这一目标，我们开发并部署了一个强大的进化系统， 方法来映射连接序列，结构，功能，调节和疾病的复杂关系， 生物医学上重要的蛋白质超家族，如蛋白激酶。我们做出了重要贡献 描述了各种蛋白激酶中变构调节的独特模式， 在受体酪氨酸激酶的一个子集中致癌激活，揭示了假激酶的调节， 以及开发新的工具和资源，以解决信令领域的数据集成挑战。我们 我建议在这些有影响力的研究的基础上，回答我们正在进行的研究中提出的关键问题， 作为：假激酶的功能是什么，激酶组的催化惰性成员，以及我们如何才能 使用假激酶来更好地预测和表征激酶的非催化功能？有哪些 保守的半胱氨酸残基在蛋白质和小分子激酶的调节位点中的功能， 在氧化还原信号传导和氧化应激反应中进行了后修饰，这些信号传导和氧化应激反应与以下因素有因果关系： 与年龄有关的疾病我们如何增强现有的预测基因组-表型的计算模型 关系使用结构信息，并可以机器学习结构增强的知识 图表揭示了患者源性突变与疾病表型之间的新关系？ 我们建议使用各种方法来回答这些问题，包括对大型 序列数据集，分子动力学模拟，机器学习，质谱，生物化学 分析和体内测定。这项工作的完成有望揭示新的变构位点的靶向 假激酶和激酶在疾病中的非催化功能，并显着推进我们的理解， 疾病和正常状态下的激酶调节机制。我们的工作将创造新的工具和资源， 知识图谱挖掘，并提供可解释的模型，用于推断连接基因组和 在个性化医疗中具有潜在应用的表型。最后，我们工作的范围和影响将 通过参与研究，扩展我们的专业工具和技术， 为研究激酶而开发的其他生物医学重要基因家族的方法， 糖基转移酶和磺基转移酶。