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.

项目总结

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