Multi-modal data integration to identify kinase substrates

多模式数据集成识别激酶底物

• 批准号: 10451941
• 负责人: Gaurav Pandey
• 金额: $ 49.85万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 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/10451941
• 关键词: Address; Binding; Biochemical; Bioinformatics; Biology; Cell Cycle; Cell Cycle Regulation; Cell physiology; Central Nervous System Diseases; Computer software; Computing Methodologies; Consensus; Data; Data Sources; Development; Disease; Drug Targeting; FDA approved; G-Protein-Coupled Receptors; Gene Expression; Genetic Transcription; Genome; Goals; Human; Human Biology; Individual; Internet; Ion Channel; Knowledge; Lighting; Malignant Neoplasms; Metabolic Diseases; Methodology; Methods; Modality; Modeling; Names; Neuraxis; Pharmacology; Phosphorylation; Phosphotransferases; Physiological; Protein Dynamics; Protein Family; Protein Kinase; Protein Kinase Interaction; Proteins; Proteome; Signal Transduction; Substrate Interaction; Testing; Training; Work; base; computer framework; data integration; data repository; data sharing; diverse data; drug development; drug discovery; experience; flexibility; improved; machine learning method; machine learning prediction; member; multidisciplinary; multimodal data; novel; predictive modeling; protein structure; small molecule; software repository; web server

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.

项目总结 激酶参与多种生理功能，如信号转导、转录、 发育和细胞周期调节。因此，蛋白激酶的失调与一系列 疾病，包括癌症、代谢性疾病和中枢神经系统疾病。60多种药物 靶向激酶已经得到FDA的批准，使其成为最具可药性的蛋白质家族之一。 尽管它们在生物医学上具有重要意义，但一大群人类蛋白激酶仍未得到充分研究。这些 蛋白质，通常被称为“暗激酶”，包括照亮可药物基因组(IDG)，有 对它们的底物(S)的了解有限，这最终决定了它们的细胞功能。要解决这个问题 挑战，我们将开发一种新的计算框架来预测激酶-底物相互作用，通过 将生物相关的多模式数据源与尖端的机器学习方法相结合。 具体地说，我们将首先从以下方面推导出量化激酶和底物之间潜在相互作用的特征 各种数据来源，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(目标1)。然后我们将开发出 使用一种名为集成的强大机器学习方法预测激酶-底物相互作用 一体化(EI；目标2)。EI基于异质合奏的概念，该合奏可以聚合 从上述不同数据来源获得的基本预测值的数量和种类不受限制，并且可以受益 从这些预测者之间的共识和多样性来看。由于其灵活性，Ei能够生产 与其他已建立的数据集成方法相比，来自多模式数据集的预测更准确，例如 对我们的项目来说也是如此。最后，我们将评估EI预测的激酶-底物相互作用。 在目标2中使用计算和实验方法开发的基于预测的模型(目标3)。我们 还将分享经过实验验证的交互作用、来自EI模型的最有信心的预测，以及 项目期间通过我们的KinaMetrix Web服务器以及其他公共服务器生成的所有数据和软件 数据和软件存储库。在它的顶峰，这个项目将产生新的和经过验证的计算 预测酶的底物的方法和软件，验证和高置信度的酶-底物 IDG深色蛋白的交互，以及共享这些产品的公共网络服务器(KinaMetrix)。我们期待着 这些产品将对研究暗蛋白激酶非常有用，特别是在IDG的努力中，以及更好地 了解激酶的功能，并提高其在药物开发工作中的利用率。我们的方法也是 预计将普遍适用于其他可药物蛋白家族，如离子通道和GPCRs。