Inferring Kinase Activity Profiles from Phosphoproteomic Data

从磷酸化蛋白质组数据推断激酶活性概况

• 批准号: 9755392
• 负责人: Kristen M Naegle
• 金额: $ 19.58万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-03 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9755392
• 关键词: Algorithms; Bayesian Analysis; Bayesian Method; Biopsy; Cells; Classification; Clinical; Computational algorithm; Consultations; Coupled; Data; Data Set; Databases; Development; Enzymes; Goals; Graph; Homeostasis; Human; Ischemia; Literature; Malignant Neoplasms; Markov chain Monte Carlo methodology; Mass Spectrum Analysis; Measurement; Measures; Modeling; Normal tissue morphology; Patient Care; Patients; Phosphorylation; Phosphorylation Site; Phosphotransferases; Proteome; Proxy; Research; Sampling; Shotguns; Signal Transduction; Site; Source; Statistical Models; Techniques; Testing; Tissue Sample; Tissues; Translating; Variant; anticancer research; base; biomarker discovery; cancer therapy; graph theory; interest; mathematical methods; open source; personalized therapeutic; phosphoproteomics; protein function; therapeutic target; tumor; tumor progression

Project Summary Phosphorylation can regulate protein function, which is a cornerstone of normal tissue development and home- ostasis. However, kinases, the enzymes that catalyze protein phosphorylation, are often dysregulated in cancer. Recently, advances have been made to measure global phosphorylation within human patient tumor samples. The hope is that this data holds the key to identifying patient-specific targets in cancer therapy. Unfortunately, challenges exist in interpreting phosphorylation data and its reflection of the underlying dysregulation of signaling networks. The goal of this project is to develop an algorithm that translates the measurements of phosphorylation in human samples to a prediction of kinase activity profiles. The kinase activity profiles could then be used to iden- tify new targets and classify tumor types. This goal will be achieved by: the development of graph-based score, based on predicted kinase-substrate relationships, interpretation of that score through statistical frameworks, and testing and improvement of the algorithms on available control and patient data.

项目摘要 磷酸化可以调节蛋白质功能，这是正常组织发育和家庭的基石- 动态平衡。然而，在癌症中，催化蛋白质磷酸化的酶--激酶--通常是不受调节的。 最近，在测量人类患者肿瘤样本中的整体磷酸化方面取得了进展。 希望这些数据能成为癌症治疗中识别患者特异性fic靶点的关键。不幸的是， 在解释磷酸化数据及其对潜在的信号调节失调的fl反映方面存在挑战 网络。这个项目的目标是开发一种算法来翻译磷酸化的测量结果。 在人类样本中预测fiLes蛋白的激酶活性。然后，可以使用fiLES蛋白的激酶活性来识别- 确定新的靶点并对肿瘤类型进行分类。这一目标将通过以下方式实现：基于图形的分数的发展， 基于预测的激酶-底物关系，通过统计框架对该分数的解释，以及 对可用控制和患者数据的算法进行测试和改进。

项目成果

KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions.

• DOI: 10.1371/journal.pcbi.1008681
• 发表时间: 2021-03
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Xue B;Jordan B;Rizvi S;Naegle KM
• 通讯作者: Naegle KM

KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data.

• DOI: 10.1038/s41467-022-32017-5
• 发表时间: 2022-07-25
• 期刊: Nature communications
• 影响因子: 16.6