Protein Knowledge Graph

Protein Knowledge Graphs (PKGs) integrate diverse protein data, including sequences, structures, functions, and interactions, into a unified network representation to improve protein analysis and prediction. Current research focuses on developing sophisticated graph neural network (GNN) architectures to learn effective protein representations from PKGs, often incorporating techniques like random walks and SkipGram models to capture complex relationships. These advancements enable more accurate predictions of protein function, structure, and interactions, with applications in drug discovery and the understanding of biological processes. The resulting improved protein representations are proving valuable for various downstream tasks, such as protein design and the identification of novel kinase-substrate interactions.