Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding.
Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding.
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作者：
Salcedo MV;Gravel N;Keshavarzi A;Huang LC;Kochut KJ;Kannan N
人类基因组中编码的534种蛋白激酶构成了一大类可药用蛋白质，其中包括研究充分和研究不足的“黑暗”成员。准确预测暗激酶功能是生物信息学的一个主要挑战。在这里，我们采用了一种图挖掘方法，使用知识图（KG）中编码的进化和功能背景来预测未充分研究的激酶的蛋白质和通路关联。我们提出了一种新的可扩展的图嵌入方法，RegPattern 2 Vec，它采用规则模式约束随机游动采样KG内的节点上下文的各个方面灵活。RegPattern 2 Vec从以激酶为中心的KG中学习激酶，相互作用伙伴，翻译后修饰，途径，细胞定位和化学相互作用的功能表示，该KG整合并概念化来自策划的异构数据资源的数据。通过将与预测相关的信息放在上下文中，RegPattern 2 Vec与其他基于随机行走的图嵌入方法相比提高了准确性和效率。我们表明，我们的模型产生的预测与使用实验验证的蛋白质-蛋白质相互作用（PPI）数据产生的途径富集数据重叠，这些数据来自公开可用的数据库和未用于训练的实验数据集。我们的模型还具有使用收集的随机游走作为生物背景来解释预测的蛋白质通路关联的优点。我们提供了34个暗激酶的高置信度的途径预测，并提出了三个案例研究，其中与预测相关的元路径分析，使生物学解释。总的来说，RegPattern 2 Vec有效地对多个节点类型进行采样，用于生物知识图上的链接预测，并且未充分研究的激酶、假激酶和已知途径之间的预测关联作为假设生成和测试的概念起点。
The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied “dark” members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases. We propose a new scalable graph embedding approach, RegPattern2Vec, which employs regular pattern constrained random walks to sample diverse aspects of node context within a KG flexibly. RegPattern2Vec learns functional representations of kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interactions from a kinase-centric KG that integrates and conceptualizes data from curated heterogeneous data resources. By contextualizing information relevant to prediction, RegPattern2Vec improves accuracy and efficiency in comparison to other random walk-based graph embedding approaches. We show that the predictions produced by our model overlap with pathway enrichment data produced using experimentally validated Protein-Protein Interaction (PPI) data from both publicly available databases and experimental datasets not used in training. Our model also has the advantage of using the collected random walks as biological context to interpret the predicted protein-pathway associations. We provide high-confidence pathway predictions for 34 dark kinases and present three case studies in which analysis of meta-paths associated with the prediction enables biological interpretation. Overall, RegPattern2Vec efficiently samples multiple node types for link prediction on biological knowledge graphs and the predicted associations between understudied kinases, pseudokinases, and known pathways serve as a conceptual starting point for hypothesis generation and testing.
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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：
14.9
作者：
Berginski ME;Moret N;Liu C;Goldfarb D;Sorger PK;Gomez SM
通讯作者：
Gomez SM
影响因子：
14.9
作者：
Davis AP;Grondin CJ;Johnson RJ;Sciaky D;Wiegers J;Wiegers TC;Mattingly CJ
通讯作者：
Mattingly CJ
影响因子：
64.5
作者：
Alarcón C;Zaromytidou AI;Xi Q;Gao S;Yu J;Fujisawa S;Barlas A;Miller AN;Manova-Todorova K;Macias MJ;Sapkota G;Pan D;Massagué J
通讯作者：
Massagué J
影响因子：
4
作者：
Brognard, John;Hunter, Tony
通讯作者：
Hunter, Tony