Prediction Method of IL-6 Inducing Peptides Based on Graph Neural Network

Interleukin-6 (IL-6) is a highly multifunctional glycoprotein factor that can regulate both innate and adaptive immunity as well as 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 action mechanisms are very important for developing immune therapies and diagnostic biomarker for the severity of diseases. Currently, the identification methods of IL-6 inducing peptides mostly use traditional machine learning, in which feature selection and extraction are rather complex, and field expert knowledge are required. In view of this problem, this paper proposes a novel graph neural network-based prediction method of IL-6 inducing peptides named SFGNN-IL6. In this method, the predicted structural characteristics of IL-6 inducing peptides are used to construct the adjacency matrix by screening the distance information according to the threshold, and the node features of amino acids are extracted using One-hot encoding, position encoding and BLOSUM62 encoding, and are then graph-represented. Moreover, 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 the classification of IL-6 inducing peptides. Experimental results validate that the proposed method is effective.