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学的巨大挑战。我们的 长期目标是使用计算和实验性的结合来应对这一挑战 方法。朝着这一目标努力，我们已经开发并部署了强大的进化系统 绘制连接序列，结构，功能，调节和疾病的复杂关系的方法 生物医学上重要的蛋白质超家族，例如蛋白激酶。我们做出了重要的贡献 描述各种蛋白激酶中变构调节的独特模式，使结构基础解密 在受体酪氨酸激酶子集中的致癌激活，揭示了假酶的调节， 并开发新的工具和资源来解决信号字段中的数据集成挑战。我们 建议以这些有影响力的研究为基础，以回答我们正在进行的研究中提出的关键问题 AS：假酶的功能是什么功能，催化型成员，我们如何才能 使用伪运动酶更好地预测和表征激酶的非催化功能？什么是 保守的半胱氨酸残基在蛋白质和小分子激酶的调节位点的功能，它们是 在氧化还原信号传导和氧化应激反应中进行了翻译后修饰，这些反应与因果关系 与年龄有关的疾病？我们如何增强现有的计算模型来预测基因组 - 晶状体 使用结构信息的关系，并可以在结构增强的知识上进行机器学习 图揭示了患者衍生的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，包括大型统计挖掘 序列数据集，分子动力学模拟，机器学习，质谱法，生化 分析和体内测定。这项工作的完成有望揭示针对目标的新变构网站 假子酶和激酶在疾病中的非催化功能，并显着提高我们对 疾病和正常状态的激酶调节机制。我们的工作将为 知识图挖掘并提供了可解释的模型，以推断基因组和 具有潜在应用的现象在个性化医学中。最后，我们工作的范围和影响将 通过参与扩展我们的专业工具和技术的研究，可以显着扩大 开发了用于研究其他生物医学重要基因家族的方法，例如 糖基转移酶和磺胺转移酶。