Multi-modal data integration to identify kinase substrates

项目概要 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，蛋白激酶的失调与一系列 疾病，包括癌症、代谢疾病和中枢神经系统疾病。超过60种药物 靶向激酶已获得 FDA 批准，使其成为最适合药物化的蛋白质家族之一。 尽管人类蛋白激酶具有生物医学重要性，但仍有大量人类蛋白激酶未被充分研究。这些 蛋白质，通常被称为“暗激酶”，包括被照亮可药物基因组（IDG），具有 对它们的底物的了解有限，而底物最终决定了它们的细胞功能。为了解决这个问题 挑战，我们将开发一个新的计算框架来预测激酶-底物相互作用 将生物相关的多模式数据源与尖端的机器学习方法相结合。 具体来说，我们将首先得出量化激酶和底物之间潜在相互作用的特征 多样化的数据源，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（目标 1）。接下来我们将开发 使用名为 Ensemble 的强大机器学习方法来预测激酶-底物相互作用 整合（EI；目标 2）。 EI 基于异构集成的概念，可以聚合 来自上述不同数据源的基本预测变量的数量和种类不受限制，并且可以受益 来自这些预测变量之间的共识和多样性。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，多模态数据集的预测更准确，例如 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 使用计算和实验方法（目标 3）在目标 2 中开发基于预测模型。我们 还将分享经过实验验证的相互作用、EI 模型最可信的预测，以及 本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。该项目最终将产生新颖且经过验证的计算 预测激酶底物、经过验证且高置信度的激酶底物的方法和软件 IDG 暗激酶的交互，以及共享这些产品的公共网络服务器 (KinaMetrix)。我们期望 这些产品对于暗激酶的研究非常有用，特别是在 IDG 的工作中，以及更好地研究暗激酶 了解激酶功能并提高其在药物开发工作中的利用率。我们的做法也是 预计普遍适用于其他可成药的蛋白质家族，例如离子通道和 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.