Prediction of Kinase-Substrate Associations Using The Functional Landscape of Kinases and Phosphorylation Sites
Prediction of Kinase-Substrate Associations Using The Functional Landscape of Kinases and Phosphorylation Sites
复制标题
利用激酶和磷酸化位点的功能景观预测激酶-底物关联
DOI：
10.1142/9789811270611_0008
复制
发表时间：
2022
影响因子：
--
通讯作者：
M. Koyuturk
中科院分区：
文献类型：
--
作者：
M. Ayati;Serhan Yılmaz;Filipa B. Lopes;Mark R. Chance;M. Koyuturk
蛋白质磷酸化是一种关键的翻译后修饰，在许多细胞过程中起着核心作用。随着生物技术的最新进展，可以在给定的样品中鉴定和定量数千个磷酸化位点，从而实现细胞信号的蛋白质组范围的筛选。然而，对于在这些实验中鉴定的大多数（> 90%）磷酸化位点，靶向这些位点的激酶是未知的。为了广泛利用现有的结构，功能，进化和上下文信息来预测激酶-底物关联（KSA），我们开发了一个基于网络的机器学习框架。我们的框架集成了大量的数据源，以表征景观的磷酸盐和激酶之间的功能关系和协会。为了构建一个磷酸-磷酸缔合网络，我们使用序列相似性，共享的生物学途径，共同进化，共同出现，并在不同的生物状态磷酸的共磷酸化。为了构建一个激酶-激酶关联网络，我们整合了蛋白质-蛋白质相互作用，共享的生物学途径和共同激酶家族的成员资格。我们使用从这些异构网络计算的节点嵌入来训练机器学习模型，以预测激酶-底物关联。我们使用PhosphositePLUS数据库进行的系统计算实验表明，所得到的算法NetKSA在整体KSA预测方面优于两种最先进的算法，包括KinomeXplorer和LinkPhinder。通过对激酶的排序进行分层，NetKSA还能够注释相对较少研究的激酶所靶向的磷酸位点。
Protein phosphorylation is a key post-translational modification that plays a central role in many cellular processes. With recent advances in biotechnology, thousands of phosphorylated sites can be identified and quantified in a given sample, enabling proteome-wide screening of cellular signaling. However, for most (> 90%) of the phosphorylation sites that are identified in these experiments, the kinase(s) that target these sites are unknown. To broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations (KSAs), we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships and associations among phosphosites and kinases. To construct a phosphosite-phosphosite association network, we use sequence similarity, shared biological pathways, co-evolution, co-occurrence, and co-phosphorylation of phosphosites across different biological states. To construct a kinase-kinase association network, we integrate protein-protein interactions, shared biological pathways, and membership in common kinase families. We use node embeddings computed from these heterogeneous networks to train machine learning models for predicting kinase-substrate associations. Our systematic computational experiments using the PhosphositePLUS database shows that the resulting algorithm, NetKSA, outperforms two state-of-the-art algorithms, including KinomeXplorer and LinkPhinder, in overall KSA prediction. By stratifying the ranking of kinases, NetKSA also enables annotation of phosphosites that are targeted by relatively less-studied kinases.
DOI：
10.1136/ebmh.11.4.102
发表时间：
2008-10
期刊：
Evidence Based Mental Health
影响因子：
--
作者：
P. Cochat;L. Vaucoret;J. Sarles
通讯作者：
P. Cochat;L. Vaucoret;J. Sarles