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

doi:10.12141/j.issn.1000-565X.240242

Abstract

Abstract:

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 dia-gnostic 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.

Key words: IL-6 inducing peptide, graph neural network, structural feature, graph attention mechanism, graph convolutional neural network

CLC Number:

TP183

CAO Ruifen, HU Weiling, LI Qiangsheng, BIN Yannan, ZHENG Chunhou. Prediction Method of IL-6 Inducing Peptides Based on Graph Neural Network[J]. Journal of South China University of Technology(Natural Science Edition), 2025, 53(5): 109-117.

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数据集	IL-6诱导肽数量/条	非IL-6诱导肽数量/条
训练集	292	2 393
测试集	73	598

阈值/nm	B	S	T	M	A_UC
0.8	0.705±0.014	0.691±0.066	0.718±0.077	0.278±0.035	0.781±0.021
0.9	0.723±0.019	0.637±0.074	0.809±0.096	0.341±0.054	0.799±0.019
1.0	0.706±0.013	0.651±0.087	0.762±0.114	0.301±0.047	0.794±0.013
1.1	0.692±0.040	0.571±0.155	0.813±0.093	0.297±0.046	0.790±0.020

GCN层数	GAT层数	B	S	T	M	A_UC
1	1	0.723±0.026	0.736±0.067	0.709±0.066	0.296±0.038	0.787±0.033
	2	0.723±0.019	0.637±0.074	0.809±0.096	0.341±0.054	0.799±0.019
	3	0.697±0.016	0.624±0.115	0.771±0.142	0.300±0.058	0.788±0.023
2	1	0.709±0.026	0.674±0.069	0.745±0.038	0.286±0.036	0.791±0.026
	2	0.720±0.032	0.718±0.059	0.722±0.093	0.313±0.062	0.794±0.016
	3	0.722±0.020	0.691±0.054	0.752±0.031	0.309±0.029	0.792±0.028

不平衡处理	B	S	T	M	A_UC
正样本随机过采样	0.778	0.643	0.913	0.488	0.868
SMOTE过采样	0.786	0.726	0.846	0.432	0.864
负样本随机欠采样	0.737	0.794	0.680	0.306	0.812
损失函数加权重	0.801	0.753	0.849	0.456	0.875

损失函数	B	S	T	M	A_UC
WCE Loss	0.801	0.753	0.849	0.456	0.875
CE Loss	0.728	0.507	0.948	0.469	0.865
Focal Loss	0.791	0.822	0.761	0.395	0.869
ASL Loss	0.765	0.740	0.791	0.374	0.862