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

项目摘要 从基因类型预测疾病表型在生物学和个性化医学中是一个巨大的挑战。我们的 长期目标是使用计算和实验相结合的方法来解决这一挑战 接近了。为了实现这一目标，我们开发并部署了强大的进化系统 绘制序列、结构、功能、调控和疾病之间复杂关系的方法 生物医学上重要的蛋白质超家族，如蛋白激酶。我们作出了重要贡献 描述各种蛋白激酶中变构调节的独特模式，破译其结构基础 在受体酪氨酸激酶的一个子集中的致癌激活，揭示了假激酶的调节， 以及开发新的工具和资源，以应对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，回答我们正在进行的研究中产生的关键问题，如 AS：假激酶系的催化惰性成员的功能是什么？我们如何 使用假性激酶来更好地预测和表征非催化功能的激酶？什么是 保守的半胱氨酸残基在蛋白质和小分子激酶调节位点上的功能 氧化还原信号和氧化应激反应中的翻译后修饰与 与年龄相关的疾病？我们如何增强现有的预测基因组现象组的计算模型？ 使用结构信息的关系，并可以对结构增强的知识进行机器学习 图表揭示了患者衍生的突变和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，包括对大型数据的统计挖掘 序列数据集、分子动力学模拟、机器学习、质谱学、生化 分析和体内检测。这项工作的完成有望揭示新的变构靶点 假性激酶和非催化激酶在疾病中的作用，并极大地促进了我们对 疾病和正常状态下的激酶调节机制。我们的工作将为以下方面创造新的工具和资源 知识图挖掘，并提供可解释的模型，用于推断将基因组和 在个性化医疗方面具有潜在应用的现象。最后，我们工作的范围和影响将 通过参与扩展我们的专门工具和技术的研究而显著扩大 为研究其他生物医学重要基因家族的激酶而开发的方法 糖基转移酶和磺基转移酶。

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.