Multi-modal data integration to identify kinase substrates

项目摘要 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，蛋白激酶的调节异常与一系列的蛋白激酶活性相关。 疾病，包括癌症、代谢疾病和中枢神经系统病症。60多种药物 靶向激酶已被FDA批准，使其成为最可药用的蛋白质家族之一。 尽管它们在生物医学上很重要，但一大群人类蛋白激酶仍然高度缺乏研究。这些 蛋白质，通常被称为“暗激酶”，包括通过照亮可药用基因组（IDG）， 对它们的底物的了解有限，这最终决定了它们的细胞功能。为了解决这个 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， 将生物相关的多模态数据源与尖端的机器学习方法相结合。 具体来说，我们将首先从以下方面获得量化激酶和底物之间潜在相互作用的特征： 不同的数据源，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 使用强大的机器学习方法Entrance预测激酶-底物相互作用 整合（EI;目标2）。EI是基于异构集成的概念，可以聚合一个 不受限制的数量和种类的基础预测来自上述不同的数据源，并可以受益于 从这些预测因素的一致性和多样性中。由于其灵活性，EI能够生产 多模态数据集的预测比其他已建立的数据集成方法更准确， 我们的项目也是如此。最后，我们将评估EI预测的激酶-底物相互作用， 基于目标2中开发的预测模型，使用计算和实验方法（目标3）。我们 还将分享实验验证的相互作用，EI模型最有信心的预测， 所有的数据和软件在这个项目中通过我们的KinaMeetings网络服务器生成，以及其他公共 数据和软件存储库。在其高潮，该项目将产生新的和验证的计算 预测激酶底物的方法和软件、经验证的和高置信度的激酶底物 IDG暗激酶的相互作用，以及共享这些产品的公共网络服务器（KinaMeetings）。我们预计 这些产品将是非常有用的研究暗激酶，特别是在IDG的努力，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 预期可普遍适用于其他可药用蛋白质家族，如离子通道和GPCR。

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.