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：
10793016
负责人：
Natarajan Kannan
金额：
$ 13.77万
依托单位：
UNIVERSITY OF GEORGIA
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

项目摘要

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.

项目摘要（不变）从基因型预测疾病表型是生物学和个性化医学。我们的长期目标是使用计算方法和实验方法的组合。努力实现这一目标，我们已经开发并部署了强大的进化系统方法来映射连接序列，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。我们做了重要的贡献，描述了各种变构调节的独特模式蛋白激酶，在一部分中解密了致癌激活的结构基础受体酪氨酸激酶，发现假酶的调节，并发展用于解决信号字段中数据集成挑战的新工具和资源。我们建议以这些有影响力的研究为基础，以回答从我们正在进行的研究，例如：伪动酶的功能是什么，催化性 - 惰性成员的成员，我们如何使用伪动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的调节位点的半胱氨酸残基，它们是在氧化还原信号传导和氧化应激反应中进行的翻译后修饰与年龄有关的疾病有因果关系吗？我们如何增强现有用于使用结构的预测基因组 - 表现关系的计算模型信息，并且可以在结构增强的知识图上进行机器学习揭示患者衍生的突变与疾病表型之间的新关系？我们建议使用包括统计的各种方法来回答这些问题挖掘大序列数据集，分子动力学模拟，机器学习，质谱，生化分析和体内测定。这项工作的完成是预计将揭示新的变构位点，用于靶向假子酶和激酶非 - 疾病中的催化功能，并显着提高我们对激酶的理解疾病和正常状态的调节机制。我们的工作将创建新工具，知识图挖掘的资源，并提供可解释的模型将基因组和现象与潜在应用联系起来的因果关系个性化医学。最后，我们工作的范围和影响将显着通过参与研究扩展我们的专业工具和技术开发了用于研究其他生物医学重要基因的方法诸如糖基转移酶和磺胺转移酶之类的家族。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

An allosteric switch between the activation loop and a c-terminal palindromic phospho-motif controls c-Src function.

DOI：
10.1038/s41467-023-41890-7
发表时间：
2023-10-17
期刊：
NATURE COMMUNICATIONS
影响因子：
16.6
作者：
Cuesta-Hernandez, Hipolito Nicolas;Contreras, Julia;Soriano-Maldonado, Pablo;Sanchez-Wandelmer, Jana;Yeung, Wayland;Martin-Hurtado, Ana;Munoz, Ines G.;Kannan, Natarajan;Llimargas, Marta;Munoz, Javier;Plaza-Menacho, Ivan
通讯作者：
Plaza-Menacho, Ivan

Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence embeddings.

DOI：
10.1093/bib/bbac599
发表时间：
2023-01-19
期刊：
Briefings in bioinformatics
影响因子：
9.5
作者：
通讯作者：

Protein kinase inhibitor selectivity "hinges" on evolution.

蛋白激酶抑制剂的选择性“取决于”进化。

DOI：
10.1016/j.str.2022.11.004
发表时间：
2022
期刊：
Structure (London, England : 1993)
影响因子：
0
作者：
Shrestha,Safal;Bendzunas,George;Kannan,Natarajan
通讯作者：
Kannan,Natarajan

Phosphorylation-dependent pseudokinase domain dimerization drives full-length MLKL oligomerization.

DOI：
10.1038/s41467-023-42255-w
发表时间：
2023-10-26
期刊：
NATURE COMMUNICATIONS
影响因子：
16.6
作者：
Meng, Yanxiang;Garnish, Sarah E.;Davies, Katherine A.;Black, Katrina A.;Leis, Andrew P.;Horne, Christopher R.;Hildebrand, Joanne M.;Hoblos, Hanadi;Fitzgibbon, Cheree;Young, Samuel N.;Dite, Toby;Dagley, Laura F.;Venkat, Aarya;Kannan, Natarajan;Koide, Akiko;Koide, Shohei;Glukhova, Alisa;Czabotar, Peter E.;Murphy, James M.
通讯作者：
Murphy, James M.

Redox Regulation of Brain Selective Kinases BRSK1/2: Implications for Dynamic Control of the Eukaryotic AMPK family through Cys-based mechanisms