Predicting variable-length ACE inhibitory peptides based on graph convolutional network

文献类型： 外文期刊

第一作者： Rong, Yating

作者： Rong, Yating;Zhao, Jinyong;Li, Ping;Yang, Huihui;Wang, Yutang;Wang, Fengzhong;Rong, Yating;Cai, Xiaoshuang;Song, Hongjie;Wang, Lili;Wang, Yehui;Yan, Xinxu;Sun, Yulin;Feng, Baolong;Wang, Yutang

作者机构：

关键词： ACE inhibitory peptides; Variable-length; Molecular graphs; Graph convolutional network

期刊名称：INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES （ 影响因子：8.5； 五年影响因子：8.7 ）

ISSN： 0141-8130

年卷期： 2024 年 282 卷

页码：

收录情况： SCI

摘要： Traditional molecular descriptors have contributed to the prediction of angiotensin I-converting enzyme (ACE) inhibitory peptides, but they often fall short in capturing the complex structure of the molecule. To address these limitations, this study introduces molecular graphs as an advanced method for peptide characterization. Peptides containing 2-10 amino acids were represented using molecular graphs, and a graph convolutional network (GCN) model was constructed to predict variable-length peptides. This model was compared with machine learning (ML) models based on molecular descriptors, including Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN), under the same benchmark. Notably, the GCN model outperformed the other models with an accuracy of 0.78, effectively identifying ACE inhibitory potential. Furthermore, the GCN model also demonstrated superior performance, exceeding existing methods with an accuracy rate of over 98 % on an independent test set. To validate our predictions, we synthesized peptides VAPE and AQQKEP with high predicted probabilities, and their IC50 values of 2.25 +/- 0.11 and 3.75 +/- 0.17 mu M, respectively, indicating potent ACE inhibitory activity. The developed GCN model presents a powerful tool for the rapid screening and identification of ACE inhibitory peptides, offering promising opportunities for developing antihypertensive components in functional foods.

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