Knowledge Representation of G-Protein-Coupled Receptor Signal Transduction Pathways

G-protein-coupled receptors (GPCRs) are the largest family of plasma membrane receptors, which can be activated by an external signal such as a ligand. Binding of GPCRs and ligands in the plasma membrane activates pathways that involve a sequence of events. Better understanding of GPCRs and the signal transduction pathways can help biologists to target new drugs or regulate many important cellular functions for diseases. In this paper, we introduce ontology-based GPCR signal transduction pathways, which are converted from manually collected pathways in PubMed papers. We applied network graph embedding and knowledge graph embedding algorithms on the pathway data to discover protein interactions in the GPCR signal transduction pathways. Experiments show that we could suggest missing or unknown pathways by analyzing the ontology-based GPCR signal transduction pathways. Moreover, we introduced ontology constraints on the GPCR pathway for predicting the missing interactions. The experimental results showed that using ontology constraints can boost the performance of knowledge graph embedding algorithms.