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

项目摘要（未变更） 从基因型预测疾病表型是生物学中的一个巨大挑战， 个性化医疗我们的长期目标是利用 计算和实验方法的结合。为实现这一目标， 我们已经开发并部署了一个强大的进化系统方法来绘制 连接序列、结构、功能、调节和疾病的复杂关系 在生物医学上重要的蛋白质超家族中，例如蛋白激酶。我们取得了 重要的贡献，描述了不同的别构调节的独特模式， 蛋白激酶，破译致癌激活的结构基础，在一个子集， 受体酪氨酸激酶，揭示假激酶的调节， 新的工具和资源，用于解决信令领域的数据集成挑战。 我们建议在这些有影响力的研究的基础上，回答以下问题： 我们正在进行的研究，如：什么是假激酶的功能，催化- 激酶组的惰性成员，以及我们如何使用假激酶来更好地预测和 表征激酶的非催化功能？保守的功能是什么 蛋白质和小分子激酶调节位点的半胱氨酸残基， 氧化还原信号传导和氧化应激反应的翻译后修饰 与年龄相关疾病有因果关系吗如何提高现有 使用结构预测基因组-表型组关系的计算模型 信息，以及在结构增强的知识图上的机器学习能否揭示 患者源性突变和疾病表型之间的新关系？我们 我建议用各种方法来回答这些问题，包括统计学方法。 大型序列数据集的挖掘，分子动力学模拟，机器学习， 质谱、生化分析和体内测定。完成这项工作是 有望揭示新的变构位点，靶向假激酶和激酶非 催化功能的疾病，并显着推进我们的理解激酶 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， 资源进行知识图挖掘，并提供可解释的模型进行推理 基因组和表型之间的因果关系， 个性化医疗最后，我们工作的范围和影响将显著扩大， 通过参与研究扩展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 如糖基转移酶和磺基转移酶家族。

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.