KSFinder-a knowledge graph model for link prediction of novel phosphorylated substrates of kinases.
KSFinder-a knowledge graph model for link prediction of novel phosphorylated substrates of kinases.
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作者：
Anandakrishnan M;Ross KE;Chen C;Shanker V;Cowart J;Wu CH
异常蛋白激酶调控导致底物磷酸化异常与几种人类疾病有关。尽管针对激酶的治疗有希望，但许多人类激酶仍未得到充分研究。大多数现有的计算工具预测磷酸化覆盖不到50%的已知人类激酶。他们利用基于蛋白质序列、基序、结构域、结构和/或功能的局部特征选择，而不考虑蛋白质的异质关系。在这项工作中，我们提出了KSFinder，这是一种通过捕获包含85%已知人类激酶的网络中蛋白质的固有关联来预测激酶-底物连接的工具。基于KSFinder的底物预测，我们还假设了两种未被充分研究的激酶的潜在作用。KSFinder使用知识图嵌入算法学习磷酸化蛋白质组知识图中的语义关系，并用低维向量表示节点。一个多层感知器（MLP）分类器被训练来识别激酶-底物连接使用嵌入的向量。KSFinder采用战略性负生成方法，消除了实体表示中的偏差，并结合了实验验证的非相互作用蛋白质对、来自不同亚细胞位置的蛋白质和随机抽样的数据。我们评估了KSFinder在四个不同数据集上的泛化能力，并将其性能与其他最先进的预测模型进行了比较。我们使用KSFinder来预测68种“暗”激酶的底物，这些底物被认为是照亮可药物基因组计划研究不足的，并使用我们的文本挖掘工具RLIMS-P以及手动管理来搜索预测的文献证据。在一个案例研究中，我们使用两种暗激酶HIPK3和CAMKK1的预测底物对它们进行了功能富集分析。KSFinder在不同的数据集上表现出比其他激酶-底物预测模型更好的性能和广义预测能力。我们确定了17个涉及未充分研究的激酶的新预测的文献证据。所有这17个预测的概率得分均≥0.7（9个为bb0 0.9, 6个为0.8-0.9,2个为0.7 - 0.8）。对93593个阴性预测（概率≤0.3）的评估发现了4个假阴性。HIPK3底物最富集的生物学过程与细胞外基质和表观遗传基因表达调控有关，而CAMKK1底物包括脂质储存调控和葡萄糖稳态。KSFinder以更高的激酶覆盖率优于当前的激酶-底物预测工具。战略性开发的底片为KSFinder提供了优越的泛化能力。我们预测了432种激酶的底物，其中68种尚未得到充分研究，并利用它们预测的底物假设了两种暗激酶的潜在功能。
Aberrant protein kinase regulation leading to abnormal substrate phosphorylation is associated with several human diseases. Despite the promise of therapies targeting kinases, many human kinases remain understudied. Most existing computational tools predicting phosphorylation cover less than 50% of known human kinases. They utilize local feature selection based on protein sequences, motifs, domains, structures, and/or functions, and do not consider the heterogeneous relationships of the proteins. In this work, we present KSFinder, a tool that predicts kinase-substrate links by capturing the inherent association of proteins in a network comprising 85% of the known human kinases. We also postulate the potential role of two understudied kinases based on their substrate predictions from KSFinder. KSFinder learns the semantic relationships in a phosphoproteome knowledge graph using a knowledge graph embedding algorithm and represents the nodes in low-dimensional vectors. A multilayer perceptron (MLP) classifier is trained to discern kinase-substrate links using the embedded vectors. KSFinder uses a strategic negative generation approach that eliminates biases in entity representation and combines data from experimentally validated non-interacting protein pairs, proteins from different subcellular locations, and random sampling. We assess KSFinder’s generalization capability on four different datasets and compare its performance with other state-of-the-art prediction models. We employ KSFinder to predict substrates of 68 “dark” kinases considered understudied by the Illuminating the Druggable Genome program and use our text-mining tool, RLIMS-P along with manual curation, to search for literature evidence for the predictions. In a case study, we performed functional enrichment analysis for two dark kinases - HIPK3 and CAMKK1 using their predicted substrates. KSFinder shows improved performance over other kinase-substrate prediction models and generalized prediction ability on different datasets. We identified literature evidence for 17 novel predictions involving an understudied kinase. All of these 17 predictions had a probability score ≥0.7 (nine at >0.9, six at 0.8–0.9, and two at 0.7–0.8). The evaluation of 93,593 negative predictions (probability ≤0.3) identified four false negatives. The top enriched biological processes of HIPK3 substrates relate to the regulation of extracellular matrix and epigenetic gene expression, while CAMKK1 substrates include lipid storage regulation and glucose homeostasis. KSFinder outperforms the current kinase-substrate prediction tools with higher kinase coverage. The strategically developed negatives provide a superior generalization ability for KSFinder. We predicted substrates of 432 kinases, 68 of which are understudied, and hypothesized the potential functions of two dark kinases using their predicted substrates.
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影响因子：
14.9
作者：
Gene Ontology Consortium
通讯作者：
Gene Ontology Consortium
影响因子：
--
作者：
Guo, Hui;Zhang, Yongxin;Liao, Zifang;Zhan, Wenzhu;Wang, Yuan;Peng, Yun;Yang, Meina;Ma, Xudai;Yin, Guogan;Ye, Lin
通讯作者：
Ye, Lin
影响因子：
4
作者：
López-Mateo I;Arruabarrena-Aristorena A;Artaza-Irigaray C;López JA;Calvo E;Belandia B
通讯作者：
Belandia B
影响因子：
6.6
作者：
Gemperle, Jakub;Dibus, Michal;Brabek, Jan
通讯作者：
Brabek, Jan
影响因子：
7.1
作者：
Ikezu, Seiko;Ingraham Dixie, Kaitlin L.;Ikezu, Tsuneya
通讯作者：
Ikezu, Tsuneya