Abstract:

Interleukin-6 (IL-6) is a highly multifunctional glycoprotein factor that can regulate both innate and adaptive immunity and various aspects of metabolism, including glycolysis, fatty acid oxidation, and oxidative phosphorylation. Many studies have shown that the expression and release of IL-6 in patients infected with viruses significantly increase, and are positively correlated with the severity of the disease. Therefore, identifying IL-6 inducing peptides and exploring their mechanisms of action is very important for developing immune therapies and diagnostic biomarker for the severity of the disease. Currently, the identification of IL-6 inducing peptides methods mostly use traditional machine learning, that require expert knowledge of the field. Therefore, this study proposes a novel IL-6 inducing peptide prediction method (SFGNN-IL6) based on graph neural networks. The predicted structural information of IL-6 inducing peptides are used to construct relation between the nodes of amino acids. The node features are extracted using one-hot encoding, position encoding, and BLOSUM62 encoding, and graph-represented using the encoded features. Then, graph attention mechanism layers and graph convolutional neural network layers are used as dual channels to separately extract features, considering both the update of node weights and the update of node information. Finally, the two types of features are fused for classification of IL-6 inducing peptides. The experimental results validate the effectiveness of the model proposed in this study.

Key words: IL-6 inducing peptides, Graph neural network, Structural information, Graph attention mechanism, Graph convolutional neural network