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

项目摘要(未更改) 从基因类型预测疾病表型是生物学和生物学领域的一项重大挑战 个性化医疗。我们的长期目标是通过 计算方法和实验方法的结合。朝着这个目标努力， 我们已经开发和部署了一种强大的进化系统方法来绘制 连接序列、结构、功能、调控和疾病的复杂关系 在生物医学上重要的蛋白质超家族中，如蛋白激酶。我们已经做出了 描述变构调节的独特模式的重要贡献 蛋白激酶，破译致癌激活的结构基础 受体酪氨酸激酶，揭示假酪氨酸酶的调节，并发展 应对信令领域数据集成挑战的新工具和资源。 我们建议在这些有影响力的研究的基础上，回答以下关键问题 我们正在进行的研究包括：假性激酶的功能是什么，催化- 的惰性成员，以及我们如何使用假蛋白激酶来更好地预测和 表征激酶的非催化功能？保守党有哪些职能？ 蛋白质和小分子激酶调节位点上的半胱氨酸残基 氧化还原信号和氧化应激反应中的翻译后修饰 与年龄相关的疾病有因果关系吗？我们如何增强现有的 利用结构模型预测基因组-表现组关系的计算模型 信息，以及在结构增强的知识图上的机器学习能否揭示 患者来源的突变与疾病表型之间的新关系？我们 建议使用包括统计学在内的各种方法来回答这些问题 大序列数据集的挖掘、分子动力学模拟、机器学习 质谱学、生化分析和活体检测。这项工作的完成情况是 有望揭示新的变构位点，用于靶向假性激酶和非激酶 在疾病中的催化作用，并极大地促进了我们对激酶的理解 疾病和正常状态下的调节机制。我们的工作将创造新的工具和 用于知识图挖掘的资源，并提供可解释的推理模型 基因组和物候组之间的因果关系及其潜在的应用 个性化医疗。最后，我们工作的范围和影响将是巨大的 通过参与研究来扩大我们的专业工具和技术 为研究其他生物医学重要基因的激酶而开发的方法 糖基转移酶和磺基转移酶等家族。

PROJECT SUMMARY (unchanged) 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.