Multi-modal data integration to identify kinase substrates

多模式数据集成识别激酶底物

基本信息

批准号：
10659156
负责人：
Gaurav Pandey
金额：
$ 50.7万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-05 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10659156
关键词：
Address Binding Biochemical Bioinformatics Biological Assay Biology Cell Cycle Regulation Cell physiology Central Nervous System Diseases Computer software Computing Methodologies Consensus Darkness Data Data Sources Databases Development Disease Drug Targeting FDA approved G-Protein-Coupled Receptors Gene Expression Genetic Transcription Genome Goals Human Human Biology Individual Ion Channel Knowledge Lighting Machine Learning Malignant Neoplasms Metabolic Diseases Methodology Methods Modality Modeling Names Pathway interactions Phosphorylation Phosphotransferases Physiological Protein Dynamics Protein Family Protein Kinase Protein Kinase Interaction Proteins Proteome Signal Transduction Substrate Interaction Techniques Testing Training Work base computer framework data integration data repository data sharing diverse data drug development drug discovery experience flexibility improved machine learning method member multidisciplinary multimodal data nervous system disorder novel pharmacologic predictive modeling programs protein structure small molecule software repository web server

Address Binding Biochemical Bioinformatics Biological Assay Biology Cell Cycle Regulation Cell physiology Central Nervous System Diseases Computer software Computing Methodologies Consensus Darkness Data Data Sources Databases Development Disease Drug Targeting FDA approved G-Protein-Coupled Receptors Gene Expression Genetic Transcription Genome Goals Human Human Biology Individual Ion Channel Knowledge Lighting Machine Learning Malignant Neoplasms Metabolic Diseases Methodology Methods Modality Modeling Names Pathway interactions Phosphorylation Phosphotransferases Physiological Protein Dynamics Protein Family Protein Kinase Protein Kinase Interaction Proteins Proteome Signal Transduction Substrate Interaction Techniques Testing Training Work base computer framework data integration data repository data sharing diverse data drug development drug discovery experience flexibility improved machine learning method member multidisciplinary multimodal data nervous system disorder novel pharmacologic predictive modeling programs protein structure small molecule software repository web server

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项目摘要

PROJECT SUMMARY Kinases are involved in a variety of physiological functions, such as signal transduction, transcription, development, and cell cycle regulation. Thus, dysregulation of protein kinases is associated with a range of diseases, including cancer, metabolic diseases, and central nervous system disorders. More than 60 drugs targeting kinases have been approved by the FDA, making them one of the most druggable protein families. Despite their biomedical importance, a large group of human protein kinases remains highly understudied. These proteins, often referred to as “dark kinases”, including by the Illuminating the Druggable Genome (IDG), have limited knowledge of their substrate(s), which ultimately determine their cellular function. To address this challenge, we will develop a novel computational framework to predict kinase-substrate interactions by combining biologically relevant multi-modal data sources with cutting-edge machine learning methodologies. Specifically, we will first derive features that quantify potential interactions between kinases and substrates from diverse data sources, such as protein structure and dynamics, gene expression profiles, protein-protein and protein-small molecule interaction networks, and evolutionary information (Aim 1). We will then develop predictors of kinase-substrate interactions using an powerful machine learning methodology named Ensemble Integration (EI; Aim 2). EI is based on the concept of heterogeneous ensembles that can aggregate an unrestricted number and variety of base predictors derived from the above diverse data sources, and can benefit from both the consensus and the diversity among these predictors. Due to its flexibility, EI is able to produce more accurate predictions from multi-modal datasets than other established data integration methodologies, as is expected for our project as well. Finally, we will evaluate the kinase-substrate interactions predicted by the EI- based predictive model developed in Aim 2 using both computational and experimental methods (Aim 3). We will also share the experimentally validated interactions, the most confident predictions from the EI model, and all the data and software generated during this project through our KinaMetrix web server, as well as other public data and software repositories. At its culmination, this project will produce novel and validated computational methods and software to predict substrates of kinases, validated and high-confidence kinase-substrate interactions for IDG dark kinases, and a public web server (KinaMetrix) to share these products. We expect that these products will be highly useful for the study of dark kinases, especially in the IDG effort, as well as to better understand kinase function and improve their utilization in drug development efforts. Our approach is also expected to be generally applicable to other druggable protein families, such as ion channels and GPCRs.

项目摘要激酶参与多种物理功能，例如信号转导，转录，开发和细胞周期调节。那就是蛋白激酶的失调与一系列包括癌症，代谢疾病和中枢神经系统疾病在内的疾病。超过60种药物靶向激酶已得到FDA的批准，使其成为最可吸毒的蛋白质家族之一。尽管具有生物医学的重要性，但大量的人类蛋白激酶仍然高度理解。这些蛋白质通常称为“黑暗激酶”，包括通过照明的可毒基因组（IDG）具有对其底物的了解有限，最终决定了其细胞功能。解决这个问题挑战，我们将开发一个新颖的计算框架，以通过将生物学相关的多模式数据源与尖端的机器学习方法相结合。具体而言，我们将首先得出能够量化激酶与底物之间潜在相互作用的特征多种数据源，例如蛋白质结构和动力学，基因表达谱，蛋白质 - 蛋白质和蛋白质 - 小分子相互作用网络和进化信息（AIM 1）。然后我们将发展使用功能强大的机器学习方法的集合的激酶 - 底物相互作用的预测指标集成（EI； AIM 2）。 EI基于可以汇总的异质合奏的概念从上述潜水员数据来源得出的不受限制数量和种类的基本预测因素，并且可以受益从这些预测指标之间的共识和多样性中。由于其灵活性，EI能够产生与其他已建立的数据集成方法相比，来自多模式数据集的更准确的预测我们的项目也期望。最后，我们将评估EI所预测的激酶 - 基底相互作用在AIM 2中使用计算和实验方法开发的基于基于的预测模型（AIM 3）。我们还将共享经过实验验证的互动，EI模型中最自信的预测以及通过我们的Kinametrix Web服务器以及其他公众，该项目期间生成的所有数据和软件数据和软件存储库。在其高潮时，该项目将产生新颖且经过验证的计算预测激酶底物的方法和软件 IDG深色激酶和公共Web服务器（Kinametrix）共享这些产品的交互。我们期望这一点这些产品对于研究深色激酶的研究将非常有用，尤其是在IDG工作中，以及更好了解激酶功能并改善其在药物开发工作中的利用。我们的方法也是预计通常适用于其他可吸毒蛋白质家族，例如离子通道和GPCR。