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
基本信息
- 批准号:10552630
- 负责人:
- 金额:$ 44.43万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AccelerationAddressAllosteric RegulationAllosteric SiteBiochemicalBiological AssayBiologyComplexComputer ModelsCysteineData SetDiabetes MellitusDiseaseGene FamilyGenomeGenotypeGoalsInflammatoryLinkMachine LearningMalignant NeoplasmsMapsMass Spectrum AnalysisMiningModelingMutationOncogenicOxidation-ReductionOxidative StressPatientsPhosphotransferasesProtein KinaseProteinsReceptor Protein-Tyrosine KinasesRegulationResourcesSignal TransductionSiteStructureSystemTechnologyWorkage relatedbiological adaptation to stressdata integrationdisease phenotypedrug discoveryglycosyltransferasehuman diseasein vivoknowledge graphmembermolecular dynamicspersonalized medicinephenomepredictive modelingprotein functionsmall moleculesulfotransferasetool
项目摘要
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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Natarajan Kannan其他文献
Natarajan Kannan的其他文献
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{{ truncateString('Natarajan Kannan', 18)}}的其他基金
Annotating dark ion-channel functions using evolutionary features, machine learning and knowledge graph mining
使用进化特征、机器学习和知识图挖掘注释暗离子通道函数
- 批准号:
10457684 - 财政年份:2022
- 资助金额:
$ 44.43万 - 项目类别:
Annotating dark ion-channel functions using evolutionary features, machine learning and knowledge graph mining (Kennady Boyd)
使用进化特征、机器学习和知识图挖掘注释暗离子通道函数 (Kennady Boyd)
- 批准号:
10809950 - 财政年份:2022
- 资助金额:
$ 44.43万 - 项目类别:
Annotating dark ion-channel functions using evolutionary features, machine learning and knowledge graph mining
使用进化特征、机器学习和知识图挖掘注释暗离子通道函数
- 批准号:
10661550 - 财政年份:2022
- 资助金额:
$ 44.43万 - 项目类别:
Annotating dark ion-channel functions using evolutionary features, machine learning and knowledge graph mining (Rayna Carter)
使用进化特征、机器学习和知识图挖掘注释暗离子通道函数 (Rayna Carter)
- 批准号:
10809931 - 财政年份:2022
- 资助金额:
$ 44.43万 - 项目类别:
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:
10793016 - 财政年份:2021
- 资助金额:
$ 44.43万 - 项目类别:
Determining the scope of prenylatable protein sequences
确定可异戊二烯化的蛋白质序列的范围
- 批准号:
10019396 - 财政年份:2019
- 资助金额:
$ 44.43万 - 项目类别:
Determining the scope of prenylatable protein sequences
确定可异戊二烯化的蛋白质序列的范围
- 批准号:
10461733 - 财政年份:2019
- 资助金额:
$ 44.43万 - 项目类别:
A data analytics framework for mining the dark kinome
用于挖掘暗激酶组的数据分析框架
- 批准号:
9915864 - 财政年份:2019
- 资助金额:
$ 44.43万 - 项目类别:
Determining the scope of prenylatable protein sequences
确定可异戊二烯化的蛋白质序列的范围
- 批准号:
10218213 - 财政年份:2019
- 资助金额:
$ 44.43万 - 项目类别:
A data analytics framework for mining the dark kinome
用于挖掘暗激酶组的数据分析框架
- 批准号:
10348826 - 财政年份:2019
- 资助金额:
$ 44.43万 - 项目类别:
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